Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Open accessAbstract
The Fourth Industrial Revolution may help many sectors and industries, whereas healthcare will be significantly impacted. Medical advances will be swifter, better and more effective, quickly providing medications to patients. It will act as a leveller for healthcare services by making them available to everybody. Medical 4.0 is the fourth medical revolution, employing emerging technologies to create significant advancements in healthcare. New medical 4.0 technology has advanced significantly, ranging from mobile computing to cloud computing, over the previous decade and is now ready to be employed as commercially accessible, networked systems. Expanding and with higher life expectancies, there is an enormous need for improved healthcare for older populations. This paper explores Medical 4.0 and its demand in the healthcare sector and discusses various progressive steps for Medical 4.0 implementation. Smart and Advanced Features of Medical 4.0 Practices are discussed diagrammatically. Medical 4.0 envisions a strongly interconnected health system. A hospital bed can be connected to the network and use patient data via the Internet of Things (IoT). Finally, this paper explores & provides the significant applications of Medical 4.0 for healthcare services. In addition to being creative, Medical 4.0 decreases the healthcare burden in affluent nations and offers good services to less developed countries, providing comprehensive and high-quality treatment. Medical 4.0 is characterised by technical discoveries and developments in the medical profession to encourage patient-centred therapy and drugs. This digital transformation, where patients' data will be electronically collected and utilised by technology to better understand and diagnose them, replaces the doctor-centric treatment techniques with a patient-centric paradigm.
Verified 2026-10-09 · 1 reference not checked yet
AI snapshot
Written from the paper’s full text.
TL;DR
This review describes Medical 4.0 as the fourth medical revolution, using emerging technologies such as IoT, AI, VR, big data, 5G, robotics, and blockchain to create interconnected, patient-centred healthcare. It discusses the need for Medical 4.0, progressive implementation steps, smart features, and applications across telemedicine, wearables, AI, data science, IoT, RPA, VR, edge computing, intelligent apps, additive manufacturing, 3D scanning, tissue engineering, big data, 5G, drones, nanomedicine, IoB, cybersecurity, and blockchain.
Key findings
- Medical 4.0 is characterised as a patient-centric digital transformation that replaces doctor-centric treatment techniques, aiming to make healthcare more available, accurate, individualised, and lower cost; no numerical outcomes are reported.
- The paper identifies progressive implementation steps: patient data collection and past medical records, processing with smart systems such as AI, machine learning, IoT, and cloud computing, then compilation of analysed data into improved patient records.
- It lists major applications including telemedicine, sensors and wearable technology, artificial intelligence, data science, IoT, robotic process automation, virtual reality, edge computing, intelligent apps, additive manufacturing, 3D scanning, tissue engineering, big data, 5G, drones, nanomedicine, Internet of Behaviours, cybersecurity, and blockchain.
- The paper states Medical 4.0 is still in its early stages and that a comprehensive picture may not yet exist.
Methods
- Literature review-based study using Scopus, Google Scholar, Science Direct, Research Gate, and other databases.
- Search terms included “Medical 4.0 technologies (All technologies)” and “Healthcare”; the most relevant papers were selected and critically discussed.
- No primary data, sample size, or quantitative analysis is reported.
Limitations
- The paper states that smart technologies have limitations, as do the humans who power them; human skill and learning curves differ, and innate strengths in traditional work may not translate proportionally when innovative technology is introduced.
- The paper states that digital transformation will be widespread but slower than the Internet’s digital disruption, and that companies are often conservative due to extensive investment cycles.
- Evident from the text: the review lacks a systematic review protocol, explicit inclusion/exclusion criteria, and empirical validation; many claims are broad and supported by narrative citation rather than reported outcomes.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Claims the paper makes
0 of 123 cited sources checked · 0 supported · 0 flagged
- Premisecitation
Medical 4.0 technologies are evolving into a patient-centred approach in which data will be provided to industry participants to develop better technology and healthcare delivery concepts.
“These technologies are evolving into a patient-centred approach in which the data will be provided to industry participants to develop better technology and healthcare delivery concepts [28,29].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [28] Yang, 2020, Homecare robotic systems for healthcare 4.0: visions and enabling technologies, IEEE J. biomed. health inform. source
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- cites [29] Hathaliya, 2020, An exhaustive survey on security and privacy issues in Healthcare 4.0, Comput. Commun. source
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- Premisecitation
AI is transforming the healthcare domain by allowing clinicians to stay focused on the most critical patient care and requirements rather than being distracted by different priorities of the same patients.
“AI is transforming the healthcare domain by allowing clinicians to stay focused on the most critical patient care and requirements rather than being distracted by different priorities of the same patients [60,61].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [60] Weaver, 2010, Forgoing medical care because of cost: assessing disparities in healthcare access among cancer survivors living in the United States, Cancer source
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- cites [61] Singh, 2020, Internet of things (IoT) applications to fight against COVID-19 pandemic, Diabetes Metabol. Syndr.: Clin. Res. Rev. source
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- Interpretationcitation
As technology progresses, more people will have access to high-quality healthcare at a lesser cost.
“As technology progresses, more people will have access to high-quality healthcare at a lesser cost.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [47][48][49]
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- Interpretationreasoning
Medical 4.0 can improve health care by offering more accurate and individualised care.
“Medical 4.0 can improve health care by offering more accurate and individualised care.”
Quote in the paperSubstantial claim - Premisecitation
AI technologies enable virtual nursing assistants to undertake various activities, from talking with patients to guiding them to the most appropriate and effective care unit.
“AI technologies enable virtual nursing assistants to undertake various activities, from talking with patients to guiding them to the most appropriate and effective care unit.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [125] Li, 2020, Developing a delivery science for artificial intelligence in healthcare, NPJ dig. med.
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- cites [126] Longoni, 2019, Resistance to medical artificial intelligence, J. Consum. Res. source
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- Premisecitation
Some ingestible sensors are used to help doctors detect patients with conditions such as irritable bowel syndrome and colon cancer more precisely.
“Some ingestible sensors are also being used to help doctors detect patients with conditions such as irritable bowel syndrome and colon cancer more precisely [113][114][115].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [113][114][115]
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- Interpretationreasoning
This technology can deliver healthcare services to patients who cannot go to a medical centre.
“This technology can deliver healthcare services to patients who cannot go to a medical centre.”
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AI has played a significant role in containing COVID-19.
“Artificial Intelligence has played a significant role in containing COVID-19.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [116][117][118][119][120]
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- Premisecitation
AI can assist doctors in detecting, predicting, and diagnosing illnesses more precisely and quickly.
“AI can assist doctors in detecting, predicting, and diagnosing illnesses more precisely and quickly.”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Researchers in healthcare can use Machine Learning and Artificial Intelligence to create various designs and assess the practicality without endangering the lives of human volunteers.
“Researchers in healthcare can use Machine Learning and Artificial Intelligence to create various designs and assess the practicality without endangering the lives of human volunteers [131,132].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [131] He, 2019, The practical implementation of artificial intelligence technologies in medicine, Nat. Med. source
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- cites [132] Guan, 2019, Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance, Chin. Med. Sci. J. source
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- Premisecitation
With improvements in Data Science, precise diagnostic and medical measures are now possible.
“However, with improvements in Data Science, precise diagnostic & medical measures are now possible.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [137] Schulz, 2019, Standards in healthcare data, Fund. Clinic. Data Sci. source
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- Premisecitation
Remote health monitoring via linked devices can save lives in the case of a medical emergency such as heart failure, diabetes, or asthma attacks.
“Remote health monitoring via linked devices can save lives in the case of a medical emergency such as heart failure, diabetes, asthma attacks, etc.. [159][160][161].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [159][160][161]
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- Premisecitation
Data Science is being explored in various areas of healthcare, including medical imaging, drug development, drug delivery, testing, genomics, predictive diagnosis, and new drug development.
“Now it is being explored in various areas of healthcare, like medical imaging, drug development, drug delivery, testing, genomics, predictive diagnosis, new drug development etc [137].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [137] Schulz, 2019, Standards in healthcare data, Fund. Clinic. Data Sci. source
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- Premisecitation
Deep learning methods in data science are being developed to detect minor defects in scanned photos.
“Deep learning methods in data science are being developed to detect such minor defects in scanned photos.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [138][139][140]
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- Premisecitation
Data science is altering the delivery of healthcare.
“Data science is also altering the delivery of healthcare.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [137] Schulz, 2019, Standards in healthcare data, Fund. Clinic. Data Sci. source
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- Premisecitation
Data science algorithms can mimic how medications would work in the body, thereby eliminating the need for lengthy experimentations.
“Data science algorithms can mimic how medications would work in the body, thereby eliminating the need for lengthy experimentations.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [143][144][145]
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- Premisecitation
Data Science and Machine Learning algorithms help improve the success rate of forecasts.
“Data Science and Machine Learning algorithms help improve the success rate of forecasts; thus, few pharmaceutical corporations are using patient information such as mutation profiles and patient metadata to get insights into upcoming diseases.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [141] Ekblaw, 2016, A Case Study for Blockchain in Healthcare: “MedRec” prototype for electronic health records and medical research data
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- cites [142] Provost, 2013, Data science and its relationship to big data and data-driven decision making, Big Data source
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- Premisecitation
IoT may automate patient care workflow with the support of a healthcare mobility solution and other innovative IoT technologies and next-generation healthcare facilities.
“With the support of a healthcare mobility solution and other innovative IoT technologies and next-generation healthcare facilities, IoT may automate patient care workflow [162][163][164][165].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [162][163][164][165]
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- Premisecitation
Virtual reality simulation is already transforming medical education and training.
“Virtual reality simulation is already transforming medical education and training.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [205] Pillai, 2019, Impact of virtual reality in healthcare: a review, Virt. Augmented Real. Mental Health Treat. source
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- cites [206] Bracq, 2019, Virtual reality simulation in nontechnical skills training for healthcare professionals: a systematic review, Simulat. Healthc. J. Soc. Med. Simulat. source
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- Premisecitation
An intelligent software robot enables healthcare firms to automate the flow of information between multiple EHRs, finance and accounting systems, other ecosystems, and medical offices.
“An intelligent software robot enables healthcare firms to automate the flow of information between multiple EHRs, finance & accounting systems, other ecosystems, medical offices, etc. [187][188][189].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [187][188][189]
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RPA aids in improved claim handling.
“RPA aids in improved claim handling [184][185][186].”
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- Premisecitation
VR technology is being used in various institutions to improve the patient experience in novel ways.
“VR technology is being used in various institutions to improve the patient experience in novel ways.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [205] Pillai, 2019, Impact of virtual reality in healthcare: a review, Virt. Augmented Real. Mental Health Treat. source
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- cites [206] Bracq, 2019, Virtual reality simulation in nontechnical skills training for healthcare professionals: a systematic review, Simulat. Healthc. J. Soc. Med. Simulat. source
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- Premisecitation
3D scanning in conjunction with 3D printing is critical in the surgical and dentistry disciplines.
“3D scanning in conjunction with 3D printing is critical in the surgical and dentistry disciplines.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [263] Stern, 2019, 3D face analysis for healthcare
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- cites [264] Liu, 2019, Toward wearable healthcare: a miniaturized 3d imager with coherent frequency-domain photoacoustics, IEEE transact. biomed. circuit. sys. source
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- Premisecitation
Worldwide, healthcare professionals, hospitals, and research organisations employ 3D printed anatomical models as reference aids for preoperative planning, intraoperative visualisation, and pre-fitting medical equipment for regular and complicated procedures.
“Worldwide, healthcare professionals, hospitals, and research organisations employ 3D printed anatomical models as reference aids for preoperative planning, intraoperative visualisation, and pre-fitting medical equipment for regular and complicated procedures.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [254][255][256][257]
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3D printed surgical cutting and drill guides are increasingly used in the medical field.
“3D printed surgical cutting and drill guides are increasingly used in the medical field.”
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3D scanners play an essential role in offering an efficient means for swiftly acquiring face and body 3D measurements without any physical contact.
“3D scanners play an essential role in offering an efficient means for swiftly acquiring face and body 3D measurements without any physical contact.”
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- Premisecitation
Dentistry and allied orthodontics such as bridges, crowns, braces, and dentures are now the significant markets for additive manufacturing in medicine.
“Dentistry and allied orthodontics such as bridges, crowns, braces, and dentures are now the significant markets for additive manufacturing in medicine.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [243][244][245]
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- Premisereasoning
One extremely essential aspect of 3D scanning is that it is entirely safe.
“One extremely essential aspect of 3D scanning is that it is entirely safe.”
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AM technology has expanded patient options for prostheses and orthotics in fit, function, and aesthetics.
“AM technology has expanded patient options for prostheses and orthotics in fit, function, and aesthetics.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [243][244][245]
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- Premisecitation
Big Data aids in the prediction of disease spread.
“Big Data aids in the prediction of disease spread.”
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- Premisecitation
With the use of big data, healthcare management becomes considerably more efficient.
“With the use of big data, healthcare management becomes considerably more efficient.”
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- Premisecitation
When big data is combined with clinical and diagnostic information, it will be possible to anticipate cancer care.
“When the data is combined with clinical and diagnostic information, it will be possible to anticipate cancer care [281][282][283].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [281][282][283]
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- Premisecitation
Big Data assists in determining the efficacy of medical treatments.
“Big Data assists in determining the efficacy of medical treatments.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [288][289][290]
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- Premisecitation
Big Data aids in the identification of successful and standardised therapies for specific ailments.
“Big Data also aids in the identification of successful and standardised therapies for specific ailments.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [288][289][290]
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- Premisecitation
Big data assists in preventing a wide variety of errors on the part of health administrators, such as incorrect doses, incorrect medications, and other human errors.
“Big data assists in preventing a wide variety of errors on the part of health administrators, such as incorrect doses, incorrect medications, and other human errors.”
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- Premisecitation
Big Data allows for comparisons of practice patterns, resource use, and hospital expenses across different healthcare groups.
“At a higher level, Big Data allows for comparisons of practice patterns, resource use, and hospital expenses across different healthcare groups, among other things.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [291][292][293]
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- Premisecitation
Remote robotic surgery may be accomplished by exploiting the interconnectivity of 5G and IoT.
“Remote robotic surgery may be accomplished by exploiting the interconnectivity of 5G and IoT.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [315][316][317]
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- Premisecitation
By integrating IoT, AI, and robots to improve patient care, 5G has established a linked healthcare environment.
“By integrating IoT, AI, and robots to improve patient care, 5G has established a linked healthcare environment.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [311][312][313][314]
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- Premisecitation
With nanotechnology, medications with high hazardous potential, such as cancer chemotherapeutic treatments, can be administered with a higher level of safety.
“With nanotechnology, medications with high hazardous potential, such as cancer chemotherapeutic treatments, can be administered with a higher level of safety.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [336][337][338][339]
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- Premisecitation
Blockchain technology makes it simple to trace a medicine from the maker to the patient.
“Blockchain technology also makes it simple to trace a medicine from the maker to the patient.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [360] Siyal, 2019, Applications of blockchain technology in medicine and healthcare: challenges and future perspectives, Cryptography source
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- cites [361] Aich, 2022, Protecting personal healthcare record using blockchain & federated learning technologies
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- Premisereasoning
Blockchain applications in healthcare are helping to preserve and defend data integrity.
“Blockchain applications in healthcare are helping to preserve and defend data integrity.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [362][363][364]
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- Premisereasoning
Blockchain ledgers can assist regulatory enforcement organisations in distinguishing between authentic and counterfeit medications.
“Blockchain ledgers can assist regulatory enforcement organisations in distinguishing between authentic and counterfeit medications.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [362][363][364]
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- Premisecitation
By allowing them to trace data provenance and any modifications made, blockchain technology can enable multiple players in the healthcare value chain to share access to their networks without jeopardising data security and integrity.
“By allowing them to trace data provenance and any modifications made, blockchain technology can enable multiple players in the healthcare value chain to share access to their networks without jeopardising data security and integrity [355][356][357].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [355][356][357]
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- Rests on an assumption: The existing healthcare digital infrastructure does not entirely support the essentially necessary security or interoperability.
- Premisecitation
Blockchain technology shortens and lowers the cost of data translation and reconciliation.
“Blockchain technology shortens and lowers the cost of data translation and reconciliation.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [365][366][367]
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- Premisecitation
Blockchain networks provide patients with the privacy and security they need.
“Blockchain networks provide patients with the privacy and security they need.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [365][366][367]
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- Classified as probably not a substantial claim. 55
- May combine two claims. 44
- The quote only partly states this claim. 1
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InterpretationreasoningThis digital transformation replaces doctor-centric treatment techniques with a patient-centric paradigm.
“This digital transformation, where patients' data will be electronically collected and utilised by technology to better understand and diagnose them, replaces the doctor-centric treatment techniques with a patient-centric paradigm.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenMedical 4.0 uses modern technologies to connect patients and healthcare professionals to the organisation and treatment technique.
“Medical 4.0 uses modern technologies to connect patients and healthcare professionals to the organisation and treatment technique.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationMedical 4.0 technology has advanced significantly over the previous decade and is now ready to be employed as commercially accessible, networked systems.
“Healthcare technologies, ranging from mobile computing to cloud computing, have advanced significantly over the previous decade and are now ready for deployment as commercially accessible.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [75] Bahl, 2020, Telemedicine technologies for confronting COVID-19 pandemic: a review, J. Ind. Integr. Manage. source
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- cites [76] Aceto, 2020, Industry 4.0 and health: internet of things, big data, and cloud computing for healthcare 4.0, J. Ind. Inform. Integr.
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Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenMedical 4.0 is characterised by technical discoveries and developments in the medical profession to encourage patient-centred therapy and drugs.
“Medical 4.0 is characterised by technical discoveries and developments in the medical profession to encourage patient-centred therapy and drugs.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationVR has demonstrated proven pain management efficacy and showed vital improvement in chronic pain patients.
“Aside from providing patients with an escape route, VR has also demonstrated proven pain management efficacy and showed vital improvement in chronic pain patients.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32][33][34]
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenMedical 4.0 decreases the healthcare burden in affluent nations and offers good services to less developed countries, providing comprehensive and high-quality treatment.
“In addition to being creative, Medical 4.0 decreases the healthcare burden in affluent nations and offers good services to less developed countries, providing comprehensive and high-quality treatment.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationThe deployment of Medical 4.0 technologies will allow for significant advancements in therapies and make them available to more persons at an affordable price.
“The advantages are vast, and the deployment of these technologies will allow for significant advancements in the therapies and make them available to more persons at an affordable price [17,20].”
Quote in the paperNot a substantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenWearables and other IoMT devices help doctors to access extensive patient information remotely.
“Wearables and other IoMT devices help doctors to access extensive patient information remotely.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPredictive analytics might help hospitals and clinics forecast admission rates to enhance staff scheduling.
“Predictive analytics might help hospitals and clinics forecast admission rates to enhance staff scheduling [34][35][36].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34][35][36]
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenMedical 4.0 is emerging as a new trend in the healthcare field, where the Healthcare sector can benefit from cloud computing that employs artificial intelligence.
“Medical 4.0 is emerging as a new trend in the healthcare field, where the Healthcare sector can benefit from cloud computing that employs artificial intelligence.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenWearable gadgets provide healthcare staff with real-time patient data while still at home.
“Wearable gadgets provide healthcare staff with real-time patient data while still at home.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenHospitals can use IoT to automate production operations and reduce the overall cost of hiring a gardener team.
“The hospitals can use IoT to automate production operations and reduce the overall cost of hiring a gardener team.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningBecause of data, medical research will grow tremendously.
“Because of data, medical research will likewise grow tremendously.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenOne of the primary advantages of telehealth over in-person options is that it limits contact between patients, healthcare personnel, and other patients while still offering a significant patient experience.
“One of the primary advantages of telehealth over in-person options is that it limits contact between patients, healthcare personnel, and other patients while still offering a significant patient experience.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenConnecting medical areas to technology equipment enable healthcare providers to introduce a more precise therapy method.
“Connecting medical areas to technology equipment enable healthcare providers to introduce a more precise therapy method.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenMedical 4.0 technologies include wearables, applications, and mobile technologies that give access to health care assistance and monitoring, growing in popularity, particularly for managing long-term and chronic illnesses.
“Medical 4.0 technologies include wearables, applications, and mobile technologies that give access to health care assistance and monitoring, growing in popularity, particularly for managing long-term and chronic illnesses.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenWearable technology predicts future ill-health endpoints in patients, and expert systems technology allows the impart of best practices.
“Wearable technology predicts future ill-health endpoints in patients, and expert systems technology allows the impart of best practices.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenA digital twin allows remote monitoring and operating equipment and systems by providing a near real-time bridge between the physical and digital worlds.
“This technology allows remote monitoring and operating equipment and systems by providing a near real-time bridge between the physical and digital worlds.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenBlockchain can significantly enhance the healthcare business.
“Blockchain is a movement that can significantly enhance the healthcare business.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationCommunication between healthcare practitioners and patients has become much easier because of advances in medical technologies.
“Communication between healthcare practitioners and patients has become much easier because of advances in medical technologies [78,79].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [78] Zheng, 2013, Emerging wearable medical devices towards personalized healthcare
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- cites [79] Jain, 2021, Adoption of fog computing in healthcare 4.0
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Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenCompanies that use digital twins for significant benefits include enhanced operations, product and service innovation, and faster time-to-market.
“Companies that use digital twins for significant benefits include enhanced operations, product and service innovation, and faster time-to-market.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationIoT devices in healthcare, such as wearable fitness bands, pulsometers, digital thermometers, and other related health equipment, elevate individuals' selfcare efforts.
“IoT devices in healthcare, such as wearable fitness bands, pulsometers, digital thermometers, and other related health equipment, elevate individuals' selfcare efforts.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningMedical 4.0 technologies monitor patients and administer individualised care.
“Medical 4.0 technologies monitor patients and administer individualised care.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone given5G can let IoT devices connect and communicate faster.
“5G can let IoT devices connect and communicate faster.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenMedical 4.0 technologies help to provide individualised therapy, address symptoms rapidly and monitor patients' health minute by minute in real-time.
“Medical 4.0 technologies help to provide individualised therapy, address symptoms rapidly and monitor patients' health minute by minute in real-time.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenTechnologies of Medical 4.0 are to improve operating performance, including health and safety performance.
“Technologies of Medical 4.0 are to improve operating performance, including health and safety performance.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe development of wearables and sensors has heralded a new age of telemedicine.
“The development of wearables and sensors has heralded a new age of telemedicine.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIoT devices capture data and transmit it for examination and effective medicine, providing healthcare practitioners with a more accurate reflection of various healthcare applications and diagnoses.
“IoT devices capture data and transmit it for examination and effective medicine, providing healthcare practitioners with a more accurate reflection of various healthcare applications and diagnoses.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningTelemedicine applications have become critical for millions of individuals worldwide regarding health promotion and illness prevention.
“Telemedicine applications have become critical for millions of individuals worldwide regarding health promotion and illness prevention.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationTelehealth services have grown in popularity in recent years, as Covid-19 has made individuals worldwide more worried about their general health.
“Telehealth services have grown in popularity in recent years, as Covid-19 has made individuals worldwide more worried about their general health [90][91][92][93][94][95].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [90][91][92][93][94][95]
Not checked in the free run.
May combine two claims.
PremisecitationAI can improve surgery speed and help doctors perform delicate incisions.
“AI can also improve surgery speed and help doctors perform delicate incisions [121][122][123][124].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [121][122][123][124]
Not checked in the free run.
May combine two claims.
PremisecitationAI has been employed to automate the most mundane and repetitive processes and reduce paperwork.
“Initially, AI has been employed to automate the most mundane and repetitive processes and reduce paperwork.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [116][117][118][119][120]
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Probably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenTelemedicine enables healthcare practitioners to examine, diagnose, and treat patients without seeing them.
“Telemedicine enables healthcare practitioners to examine, diagnose, and treat patients without seeing them.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAI-powered medical chatbots can provide patients with instant answers to many health-related queries and guide patients, saving unnecessary trips to the doctor and healthcare centre.
“AI-powered medical chatbots service can provide patients with instant answers to many of their healthrelated queries and guide patients with any potential problems, saving on unnecessary trips to the doctor and healthcare centre.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [121][122][123][124]
Not checked in the free run.
May combine two claims.
PremisecitationAI can analyse enormous amounts of data to create knowledge databases and ease evaluation and advice for each patient, improving clinical decision support.
“Artificial intelligence can analyse the enormous amounts of data, which may be used to create knowledge databases and ease evaluation and advice for each patient, improving clinical decision support.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [121][122][123][124]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWireless sensors from Sensor Metrix are used in hospital refrigerators, freezers, and labs to keep blood samples, pharmaceuticals, and other items at the right temperature.
“Wireless sensors from Sensor Metrix are used in hospital refrigerators, freezers, and labs to keep blood samples, pharmaceuticals, and other items at the right temperature [100][101][102].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [100][101][102]
Not checked in the free run.
May combine two claims.
PremisecitationPredictive analytics can be helpful to improve patient care, chronic illness management, the efficiency of supply chains and pharmaceutical logistics.
“This predictive analytics can be helpful to improve patient care, chronic illness management, the efficiency of supply chains and pharmaceutical logistics.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [143][144][145]
Not checked in the free run.
May combine two claims.
PremisereasoningIoT offers real-time alerting, tracking, and monitoring, allowing for hands-on treatments, improved accuracy, timely intervention by doctors, and improved overall patient care delivery results.
“As a result, IoT offers real-time alerting, tracking, and monitoring, allowing for hands-on treatments, improved accuracy, timely intervention by doctors, and improved overall patient care delivery results.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [166][167][168][169]
Not checked in the free run.
May combine two claims.
PremisecitationInternet of Things (IoT) in healthcare offers interoperability, machine-to-machine connection, information sharing, and data migration, which contribute to the effectiveness of healthcare service delivery.
“Internet of Things (IoT) in healthcare offers interoperability, machine-to-machine connection, information sharing, and data migration, which contribute to the effectiveness of healthcare service delivery.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [155][156][157][158]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationData Science is critical in monitoring patients' health and informing essential measures to avoid potential illnesses.
“Data Science is critical in monitoring patients' health and informing essential measures to be made to avoid potential illnesses from occurring.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [146] Kostkova, 2016, Who owns the data? Open data for healthcare, Front. Public Health source
Not checked in the free run.
- cites [147] Kim, 2017, Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare, Inf. Dev. source
Not checked in the free run.
May combine two claims.
PremisecitationMedical IoT devices collect vital indicators of any ailment and transmit that data to clinicians for real-time surveillance, while mobile applications and smart sensors notify individuals about key portions.
“Medical IoT devices collect vital indicators of any ailment and transmit that data to clinicians for real-time surveillance, while mobile applications and smart sensors notify individuals about key portions [155][156][157][158].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [155][156][157][158]
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOne of IoT's most critical difficulties in healthcare is data security and privacy.
“One of IoT's most critical difficulties in healthcare is data security and privacy.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [170][171][172][173][174]
Not checked in the free run.
May combine two claims.
PremisecitationRPA may automate registration procedures, maintain and manage medical information, and perform repetitive data entry.
“RPA may automate registration procedures, maintain and manage medical information, and perform repetitive data entry.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [184][185][186]
Not checked in the free run.
May combine two claims.
PremisecitationBy automating time-consuming and everyday activities such as data digitisation and payable accounts, RPA will improve billing performance, reduce write-offs and save healthcare providers.
“By automating time-consuming and everyday activities such as data digitisation and payable accounts, RPA will improve billing performance, reduce write-offs and save healthcare providers.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [190][191][192]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEdge computing can be vital in times like the flu pandemic, where clinicians can monitor patients' vitals from remote areas and devise an action plan.
“Edge computing can be vital in times like the flu pandemic, where clinicians can monitor patients' vitals from remote areas and devise an action plan.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [219] Sodhro, 2019, Mobile edge computing based QoS optimization in medical healthcare applications, Int. J. Inf. Manag.
Not checked in the free run.
- cites [220] Ray, 2019, Edge computing for Internet of Things: a survey, e-healthcare case study and future direction, J. Netw. Comput. Appl. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationProviding adequate healthcare to distant rural regions has always been difficult.
“Providing adequate healthcare to distant rural regions has always been difficult [219,220].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [219] Sodhro, 2019, Mobile edge computing based QoS optimization in medical healthcare applications, Int. J. Inf. Manag.
Not checked in the free run.
- cites [220] Ray, 2019, Edge computing for Internet of Things: a survey, e-healthcare case study and future direction, J. Netw. Comput. Appl. source
Not checked in the free run.
May combine two claims.
PremisecitationVR-enabled virtual clinics have become more popular, and treating physical disorders at home using VR equipment yields promising results and is being expanded to other physical diseases.
“VR-enabled virtual clinics have become more popular; treating physical disorders at home using VR equipment yields promising results and is being expanded to other physical diseases [205,206].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [205] Pillai, 2019, Impact of virtual reality in healthcare: a review, Virt. Augmented Real. Mental Health Treat. source
Not checked in the free run.
- cites [206] Bracq, 2019, Virtual reality simulation in nontechnical skills training for healthcare professionals: a systematic review, Simulat. Healthc. J. Soc. Med. Simulat. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationExposure therapy is now considered a standard treatment method for several mental illnesses.
“Exposure therapy is now considered a standard treatment method for several mental illnesses [210][211][212][213].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [210][211][212][213]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe biomedical and biotechnology domains are critical to developing successful regeneration therapeutics.
“The biomedical and biotechnology domains are critical to developing successful regeneration therapeutics [268,269].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [268] Fernandes, 2018, Bioactive glasses and glass ceramics for bone regeneration and tissue engineering healthcare applications, Materials source
Not checked in the free run.
- cites [269] Xu, 2015, Bone tissue engineering: electrospun polycaprolactone 3D nanofibrous scaffold with interconnected and hierarchically structured pores for bone tissue engineering (Adv. Healthcare Mater. 15/2015), Adv. Healthc. Mater. source
Not checked in the free run.
May combine two claims.
PremisecitationAdditive Manufacturing (AM) creates complex structures that vary in complexity, customisation, lightweight, strength, and speed for medical devices and other essential development.
“Additive Manufacturing (AM) creates complex structures that vary in complexity, customisation, lightweight, strength, and speed, as additive manufacturing for medical devices and other essential development.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [243][244][245]
Not checked in the free run.
May combine two claims.
PremisecitationBy keeping many critical processing jobs on network edge devices, healthcare IT infrastructures may still benefit from acquiring health-related data while also receiving quick, real-time analytics to detect and respond to health problems.
“By keeping many critical processing jobs on network edge devices, healthcare IT infrastructures may still benefit from acquiring healthrelated data while also receiving quick, real-time analytics to detect and respond to health problems.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [224][225][226][227]
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIntelligent apps provide a new approach to tailored health, bolstered by advanced AI, human medical expertise, and the power of mobile technology.
“These apps provide a new approach to tailored health, bolstered by advanced AI, human medical expertise, and the power of mobile technology.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [233][234][235]
Not checked in the free run.
May combine two claims.
Premisecitation3D scanning provides doctors with a precise image of their patients and thoroughly researches potential treatments without physically testing them on the patient.
“3D scanning provides doctors with a precise image of their patients and thoroughly researches potential treatments without physically testing them on the patient.”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
PremisecitationPopulation movements may be predicted using the GPS tracker, and the transmission of the infection can be limited.
“Population movements may be predicted using the GPS tracker, and the transmission of the infection can be limited.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [284][285][286][287]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTissue engineering helps functionalise tissues by mixing scaffolds, cells, and physiologically active chemicals.
“It helps functionalising tissues by mixing scaffolds, cells, and physiologically active chemicals [270,271].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [270] Archer, 2005, Why tissue engineering needs process engineering, Nat. Biotechnol. source
Not checked in the free run.
- cites [271] Sridhar, 2015, Tissue engineering: development of a cellularly degradable PEG hydrogel to promote articular cartilage extracellular matrix deposition (Adv. Healthcare Mater. 5/2015), Adv. healthc. mater. source
Not checked in the free run.
May combine two claims.
PremisecitationNon-therapeutic uses of tissue engineering include using tissues as biosensors to detect biological or chemical danger agents and tissue chips that may assess experimental medicine's toxicity.
“Non-therapeutic uses include using tissues as biosensors to detect biological or chemical danger agents and tissue chips that may assess experimental medicine's toxicity [278][279][280].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [278][279][280]
Not checked in the free run.
May combine two claims.
PremisecitationBig data contributes to better results and lowers costs through improved disease management strategies.
“It contributes to better results and lowers costs through improved disease management strategies.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [291][292][293]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe pace, complexity, and volume of data flow and business operations will be boosted in the 5G era.
“However, the pace, complexity, and volume of data flow and business operations will be boosted in this new era [303][304][305].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [303][304][305]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNanotechnology is beneficial for healthcare applications in developing countries.
“Nanotechnology is beneficial for healthcare applications in developing countries [336][337][338][339].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [336][337][338][339]
Not checked in the free run.
The quote only partly states this claim.
PremisecitationBig data will be highly beneficial to insurance firms by preventing a broad spectrum of insurance fraud claims.
“It will also be highly beneficial to insurance firms.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [298][299][300][301]
Not checked in the free run.
May combine two claims.
PremisecitationAR-powered medical devices and realtime data readings can remotely coach surgeons during surgery.
“AR-powered medical devices and realtime data readings can remotely coach surgeons during surgery.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [315][316][317]
Not checked in the free run.
May combine two claims.
PremisecitationDrones can transfer medicine within hospital boundaries, transmit blood between hospital buildings, and provide elderly patients with gadgets to help them age in situ.
“They can transfer medicine within hospital boundaries, transmit blood between hospital buildings, and provide elderly patients with gadgets to help them age in situ [329][330][331].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [329][330][331]
Not checked in the free run.
May combine two claims.
PremisecitationNanoparticles can be used in various medical applications, including novel diagnostic tools, imaging agents and procedures, targeted medication delivery, medicines, bio-implants, and tissue engineering.
“Nanoparticles can be used in various medical applications, including novel diagnostic tools, imaging agents and procedures, targeted medication delivery, medicines, bio-implants, and tissue engineering [332][333][334][335].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [332][333][334][335]
Not checked in the free run.
May combine two claims.
PremisecitationThe cloud's fast, frictionless scalability allows it to accommodate massive volumes of data involved in contemporary healthcare while utilising computing and storage resources as a service, enabling healthcare companies to react swiftly and cost-effectively to changing demands.
“The cloud's fast, frictionless scalability allows it to accommodate the massive volumes of data involved in contemporary healthcare while utilising computing and storage resources as a service, enabling healthcare companies to react swiftly and cost-effectively to changing demands [306][307][308].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [306][307][308]
Not checked in the free run.
May combine two claims.
PremisecitationNanostructure scaffolds are used for tissue replacement, and nanostructures that allow transport across biological barriers are novel nanotechnology applications.
“Nanostructure scaffolds are used for tissue replacement, and nanostructures that allow transport across biological barriers are novel nanotechnology applications.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [340][341][342][343]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWith the arrival of 5G, healthcare businesses will have access to a more excellent range of apps, data, and infrastructure, allowing them to gain a competitive edge.
“With the arrival of 5G, healthcare businesses will have access to a more excellent range of apps, data, and infrastructure, allowing them to gain a competitive edge [302].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [302] Latif, 2017, How 5g wireless (and concomitant technologies) will revolutionize healthcare?, Future Internet source
Not checked in the free run.
May combine two claims.
Premisecitation5G will enable workloads to be provided from the cloud, lowering healthcare costs while increasing treatment quality and patient outcomes.
“These are just a few of the workloads that 5G will enable to be provided from the cloud, lowering healthcare costs while increasing treatment quality and patient outcomes.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [309] Ahad, 2019, 5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions, IEEE Access source
Not checked in the free run.
- cites [310] Hossain, 2017, Emotion-aware connected healthcare big data towards 5G, IEEE Internet Things J. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationRapid delivery of vaccinations, treatments, and supplies to the point of infection might end outbreaks of potentially fatal infectious illnesses.
“Rapid delivery of vaccinations, treatments, and supplies to the point of infection might end outbreaks of potentially fatal infectious illnesses.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [318][319][320]
Not checked in the free run.
May combine two claims.
PremisecitationDrones provide for the delivery of blood, vaccinations, birth control, snakebite serum, and other medical supplies to remote regions and the capacity to reach victims in need of rapid medical treatment within minutes.
“Drones provide for the delivery of blood, vaccinations, birth control, snakebite serum, and other medical supplies to remote regions and the capacity to reach victims in need of rapid medical treatment within minutes.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [329][330][331]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisereasoningBecause data breaches in health care have a significant financial impact, health informatics and other experts play a vital role in ensuring that medical organisations stay safe.
“Because data breaches in health care have a significant financial impact, health informatics and other experts play a vital role in ensuring that medical organisations stay safe.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [347][348][349][350]
Not checked in the free run.
- Rests on an assumption: Data breaches in health care have a significant financial impact.
May combine two claims.
PremisecitationBlockchain increases a drug's traceability as it passes through the supply chain and aids in the prevention of drug counterfeiting.
“It increases a drug's traceability as it passes through the supply chain and aids in the prevention of drug counterfeiting.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [360] Siyal, 2019, Applications of blockchain technology in medicine and healthcare: challenges and future perspectives, Cryptography source
Not checked in the free run.
- cites [361] Aich, 2022, Protecting personal healthcare record using blockchain & federated learning technologies
Not checked in the free run.
May combine two claims.
PremisecitationBlockchain technology can secure data integrity and enable collaboration among increasing healthcare trends.
“This technology can secure data integrity and enable collaboration among increasing healthcare trends [358,359].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [358] Chelladurai, 2022, A novel blockchain-based electronic health record automation system for healthcare, J. Ambient Intell. Hum. Comput. source
Not checked in the free run.
- cites [359] Hölbl, 2018, A systematic review of the use of blockchain in healthcare, Symmetry source
Not checked in the free run.
May combine two claims.
PremisecitationA smartphone health app can alert potentially unsafe situations and suggest behavioural changes that might result in a more positive and desirable outcome.
“The app can alert potentially unsafe situations and suggest behavioural changes that might result in a more positive and desirable outcome [344,346].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [344] Javaid, 2021, Internet of behaviours (IoB) and its role in customer services, Sens. Int. source
Not checked in the free run.
- cites [346] Hampshire, 2021, Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom, Nat. Commun.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCybercriminals use malware and ransomware to take down individual devices, servers, or whole networks.
“Cybercriminals use malware and ransomware to take down individual devices, servers, or whole networks.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [347][348][349][350]
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBlockchain technology can accelerate innovation in preventative care and community-based healthcare solutions.
“Blockchain technology can accelerate innovation in preventative care and community-based healthcare solutions.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [358] Chelladurai, 2022, A novel blockchain-based electronic health record automation system for healthcare, J. Ambient Intell. Hum. Comput. source
Not checked in the free run.
- cites [359] Hölbl, 2018, A systematic review of the use of blockchain in healthcare, Symmetry source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationThe IoB can become a potent marketing and sales tool for businesses and organisations.
“The IoB can become a potent marketing and sales tool for businesses and organisations [344,345].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [344] Javaid, 2021, Internet of behaviours (IoB) and its role in customer services, Sens. Int. source
Not checked in the free run.
- cites [345] Bakhshi, 2019, Securing Internet of bio-Nano things: ML-enabled parameter profiling of bio-cyber interfaces
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationCompanies that have gotten to know us through IoT data may now influence our behaviour with IoB data.
“Companies that have gotten to know us through IoT data may now influence our behaviour with IoB data.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [344] Javaid, 2021, Internet of behaviours (IoB) and its role in customer services, Sens. Int. source
Not checked in the free run.
- cites [346] Hampshire, 2021, Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom, Nat. Commun.
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenMedical 4.0 technologies can bring obstacles, particularly in successfully utilising the rich insights gathered to enhance healthcare outcomes.
“Medical 4.0 technologies can bring obstacles, particularly in successfully utilising the rich insights gathered to enhance healthcare outcomes.”
Quote in the paperNot a substantial claim- Rests on an assumption: Medical 4.0 is still in its early stages, and we may not have a comprehensive picture.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenMedical 4.0 technologies are considerably altering our interactions with medical devices.
“These technologies are considerably altering our interactions with medical devices.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenOnline education for healthcare professionals has decreased the need to travel large distances and spend time away from their jobs.
“Online education for healthcare professionals has decreased the need to travel large distances and spend time away from their jobs.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenArtificial intelligence can assist in identifying viral fragments with the qualities required to achieve various objectives.
“Artificial intelligence can assist in identifying viral fragments with the qualities required to achieve various objectives.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe ongoing COVID-19 pandemic has resulted in a change away from the old paradigm and toward a more dynamic one in which healthcare staff may treat patients remotely.
“This, combined with the ongoing COVID-19 pandemic, has resulted in a change away from the old paradigm and toward a more dynamic one in which healthcare staff may treat patients remotely.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenMachine learning allows breakthroughs that humans would not otherwise be able to attain.
“Machine learning allows breakthroughs that humans would not otherwise be able to attain.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningVR can considerably aid healthcare practitioners in their delivery of care.
“VR can considerably aid healthcare practitioners in their delivery of care.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenPatient-centred AI improves results, costs, and experiences.
“Patient-centred AI improves results, costs, and experiences.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenAllowing patients to participate in their treatment will offer clinicians a wealth of information.
“Allowing patients to participate in their treatment will offer clinicians a wealth of information.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
There is an enormous need for improved healthcare for older populations due to expanding and higher life expectancies.
“Expanding and with higher life expectancies, there is an enormous need for improved healthcare for older populations.”
The paper relies on this without support.
Patients can entrust sensitive personal information to healthcare professionals.
“Patients can entrust sensitive personal information to healthcare professionals.”
The paper relies on this without support.
Most healthcare providers have begun growing to produce to give fresh meals and have eliminated as many items as feasible.
“Most healthcare providers have begun growing to produce to give fresh meals and have eliminated as many items as feasible.”
The paper relies on this without support.
Combining wearable monitors with other data sensor sources saves money and time and allows older patients and others with long-term health concerns to effectively track their progress and difficulties.
“They save money and time by combining wearable monitors with other data sensor sources, allowing older patients and others with long-term health concerns to effectively track their progress and difficulties.”
The paper relies on this without support.
Smart monitoring can aid in detecting early symptoms of illness, resulting in better treatment approaches and prevention.
“Smart monitoring can aid in detecting early symptoms of illness, resulting in better treatment approaches and prevention.”
The paper relies on this without support.
The most significant advantage of AI and healthcare technology is that they offer correct findings more quickly.
“The most significant advantage of AI and healthcare technology is that they offer correct findings more quickly.”
The paper relies on this without support.
Data science is critical in monitoring patients' health and informing essential measures to avoid potential illnesses.
“Data Science is critical in monitoring patients' health and informing essential measures to be made to avoid potential illnesses from occurring.”
The paper relies on this without support.
Smartphone applications enrich human lives by managing personal and professional relationships and activities practically, engagingly, and dynamically.
“Smartphone applications enrich human lives by managing personal/ professional relationships/activities practically, engaging, and dynamic.”
The paper relies on this without support.
The demand for efficient regeneration therapies is increasing due to the large ageing population, environmental concerns, rising lifestyle problems such as stress, obesity, and diabetes, as well as an increase in trauma cases.
“The demand for efficient regeneration therapies is increasing due to the large ageing population, environmental concerns, rising lifestyle problems such as stress, obesity, and diabetes, as well as an increase in trauma cases.”
The paper relies on this without support.
The existing healthcare digital infrastructure does not entirely support the essentially necessary security or interoperability.
“The existing system does not entirely support the essentially necessary security or interoperability.”
The paper relies on this without support.
Data breaches in health care have a significant financial impact.
“Because data breaches in health care have a significant financial impact”
The paper relies on this without support.
Protected health information is among individuals' most sensitive private data.
“Because protected health information is among individuals' most sensitive private data”
The paper relies on this without support.
Medical 4.0 is still in its early stages, and we may not have a comprehensive picture.
“Indeed, Medical 4.0 is still in its early stages, and we may not have a comprehensive picture.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Improper introduction of AI technology may lead to stress, anxiety, and the development of negative attitudes among nurses toward such technology.
“It may lead to stress, anxiety, and the development of negative attitudes among nurses toward such technology if it is not properly introduced.12,15,18”
From Exploring nurses’ awareness and attitudes toward artificial intelligence: implications for nursing practice (2024) · cites this paper as [18]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Telemedicine and telehealth are defined both as equivalent and as distinct
MinorSection 8.1 presents telehealth as another name for telemedicine, then says telehealth is broader and includes non-clinical services. Those definitions do not describe the same scope.
“Telemedicine, often known as telehealth or e-medicine, is the remote delivery of healthcare services such as tests and consultations.”
“Telehealth is a broader phrase that includes non-clinical services such as remote patient monitoring and personal medical training.”
Cites
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
The Internet of Things for Health Care: A Comprehensive Survey
Islam, S. M. R., Kwak, D., Kabir, M. H., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,199 · see citing works
Big data analytics in healthcare: promise and potential
Raghupathi, W., & Raghupathi, V. (2014) · Health Information Science and Systems · doi:10.1186/2047-2501-2-3 · Cited by 3,034 · see citing works
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396 · see citing works
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203 · see citing works
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015 · see citing works
Big data in healthcare: management, analysis and future prospects
Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0217-0 · Cited by 1,747 · see citing works
Telemedicine for Healthcare: Capabilities, Features, Barriers, and Applications
Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021) · Sensors International · doi:10.1016/j.sintl.2021.100117 · Cited by 1,638 · see citing works
Self-citationThe rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636 · see citing works
Nanotechnology in Drug Delivery and Tissue Engineering: From Discovery to Applications
Shi, J., Votruba, A. R., Farokhzad, O. C., & Langer, R. (2010) · Nano Letters · doi:10.1021/nl102184c · Cited by 1,620 · see citing works
Wang, Y., Kung, L., & Byrd, T. A. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2015.12.019 · Cited by 1,571 · see citing works
Data science and its relationship to big data and data-driven decision making
Provost, F., & Fawcett, T. (2013) · Big Data · doi:10.1089/big.2013.1508 · Cited by 1,568 · see citing works
Wearable Sensors for Remote Health Monitoring
Majumder, S., Mondal, T., & Deen, M. (2017) · Sensors · doi:10.3390/s17010130 · Cited by 1,375 · see citing works
COVID-19 transforms health care through telemedicine: Evidence from the field
Mann, D. M., Chen, J., Chunara, R., Testa, P. A., & Nov, O. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa072 · Cited by 1,361 · see citing works
Disease Prediction by Machine Learning Over Big Data From Healthcare Communities
Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L. (2017) · IEEE Access · doi:10.1109/access.2017.2694446 · Cited by 1,311 · see citing works
Blockchain technology in healthcare: The revolution starts here
Mettler, M. (2016) · 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2016.7749510 · Cited by 1,237 · see citing works
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.014 · Cited by 1,198 · see citing works
Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities
Baker, S., Xiang, W., & Atkinson, I. (2017) · IEEE Access · doi:10.1109/access.2017.2775180 · Cited by 1,147 · see citing works
Blockchain technology in healthcare: a systematic review
Agbo, C. C., Mahmoud, Q. H., & Eklund, J. M. (2019) · Healthcare · doi:10.3390/healthcare7020056 · Cited by 1,131 · see citing works
Overview of artificial intelligence in medicine
Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_440_19 · Cited by 1,106 · see citing works
Chau, P. Y. K., & Hu, P. J. (2002) · Information & Management · doi:10.1016/s0378-7206(01)00098-2 · Cited by 1,098 · see citing works
Lee, D., & Yoon, S. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010271 · Cited by 1,017 · see citing works
Blockchain-based electronic healthcare record system for healthcare 4.0 applications
Tanwar, S., Parekh, K., & Evans, R. D. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102407 · Cited by 977 · see citing works
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948 · see citing works
The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery
Swan, M. (2013) · Big Data · doi:10.1089/big.2012.0002 · Cited by 931 · see citing works
Medical Internet of Things and Big Data in Healthcare
Dimitrov, D. V. (2016) · Healthcare Informatics Research · doi:10.4258/hir.2016.22.3.156 · Cited by 927 · see citing works
Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0
Aceto, G., Persico, V., & Pescapè, A. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100129 · Cited by 916 · see citing works
Blockchain in healthcare applications: Research challenges and opportunities
McGhin, T., Choo, K. R., Liu, C. Z., & He, D. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.02.027 · Cited by 890 · see citing works
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies
Qadri, Y. A., Nauman, A., Zikria, Y. B., Vasilakos, A. V., & Kim, S. W. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2973314 · Cited by 853 · see citing works
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
Asan, O., Bayrak, A. E., & Choudhury, A. (2020) · Journal of Medical Internet Research · doi:10.2196/15154 · Cited by 825 · see citing works
Nanomedicine: Developing smarter therapeutic and diagnostic modalities☆
Farokhzad, O. C., & Langer, R. (2006) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2006.09.011 · Cited by 801 · see citing works
Use of Telemedicine and Virtual Care for Remote Treatment in Response to COVID-19 Pandemic
Jnr., B. A. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01596-5 · Cited by 800 · see citing works
Mental health care for medical staff and affiliated healthcare workers during the COVID-19 pandemic
Walton, M., Murray, E., & Christian, M. D. (2020) · European Heart Journal: Acute Cardiovascular Care · doi:10.1177/2048872620922795 · Cited by 791 · see citing works
A Systematic Review of the Use of Blockchain in Healthcare
Hölbl, M., Kompara, M., Kamišalić, A., & Nemec Zlatolas, L. (2018) · Symmetry · doi:10.3390/sym10100470 · Cited by 780 · see citing works
Wearable Chemical Sensors: Present Challenges and Future Prospects
Bandodkar, A. J., Jeerapan, I., & Wang, J. (2016) · ACS Sensors · doi:10.1021/acssensors.6b00250 · Cited by 778 · see citing works
Industry 4.0 technologies and their applications in fighting COVID-19 pandemic
Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.032 · Cited by 758 · see citing works
Self-citationThe internet of things in healthcare: An overview
Yuehong, Y. I. N., Zeng, Y., Chen, X., & Fan, Y. (2016) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2016.03.004 · Cited by 745 · see citing works
Big healthcare data: preserving security and privacy
Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2018) · Journal Of Big Data · doi:10.1186/s40537-017-0110-7 · Cited by 741 · see citing works
Smart healthcare: making medical care more intelligent
Tian, S., Yang, W., Grange, J. M. L., Wang, P., Huang, W., & Ye, Z. (2019) · Global Health Journal · doi:10.1016/j.glohj.2019.07.001 · Cited by 716 · see citing works
Blockchain for healthcare data management: opportunities, challenges, and future recommendations
Yaqoob, I., Salah, K., Jayaraman, R., & Al-Hammadi, Y. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05519-w · Cited by 687 · see citing works
Blockchain in healthcare and health sciences—A scoping review
Hasselgren, A., Kralevska, K., Gligoroski, D., Pedersen, S. A., & Faxvaag, A. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.104040 · Cited by 676 · see citing works
A Survey on Internet of Things and Cloud Computing for Healthcare
Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2019) · Electronics · doi:10.3390/electronics8070768 · Cited by 667 · see citing works
Internet of things (IoT) applications to fight against COVID-19 pandemic
Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.041 · Cited by 664 · see citing works
Self-citationArtificial intelligence-enabled healthcare delivery
Reddy, S., Fox, J., & Purohit, M. P. (2019) · Journal of the Royal Society of Medicine · doi:10.1177/0141076818815510 · Cited by 635 · see citing works
Enhancing the quality of life through wearable technology
Park, S., & Jayaraman, S. (2003) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2003.1213625 · Cited by 615 · see citing works
Artificial Intelligence in Healthcare: Review and Prediction Case Studies
Rong, G., Mendez, A., Assi, E. B., Zhao, B., & Sawan, M. (2020) · Engineering · doi:10.1016/j.eng.2019.08.015 · Cited by 555 · see citing works
Applications of blockchain technology in medicine and healthcare: challenges and future perspectives
Siyal, A. A., Junejo, A. Z., Zawish, M., Ahmed, K., Khalil, A., & Soursou, G. (2019) · Cryptography · doi:10.3390/cryptography3010003 · Cited by 531 · see citing works
Fog computing for Healthcare 4.0 environment: Opportunities and challenges
Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.08.015 · Cited by 505 · see citing works
Additive Manufacturing Processes in Medical Applications
Salmi, M. (2021) · Materials · doi:10.3390/ma14010191 · Cited by 504 · see citing works
Concurrence of big data analytics and healthcare: a systematic review
Mehta, N., & Pandit, A. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.013 · Cited by 494 · see citing works
Automation of a Business Process Using Robotic Process Automation (RPA): A Case Study
Aguirre, S., & Rodríguez, A. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-66963-2_7 · Cited by 486 · see citing works
Cybersecurity in healthcare: A narrative review of trends, threats and ways forward
Coventry, L., & Branley-Bell, D. (2018) · Maturitas · doi:10.1016/j.maturitas.2018.04.008 · Cited by 482 · see citing works
Cybersecurity in healthcare: A systematic review of modern threats and trends
Kruse, C. S., Frederick, B., Jacobson, T., & Monticone, D. K. (2016) · Technology and Health Care · doi:10.3233/thc-161263 · Cited by 479 · see citing works
Fedorovich, N. E., Alblas, J., de Wijn, J. R., Hennink, W. E., Verbout, A. J., & Dhert, W. J. A. (2007) · Tissue Engineering · doi:10.1089/ten.2006.0175 · Cited by 467 · see citing works
An exhaustive survey on security and privacy issues in Healthcare 4.0
Hathaliya, J. J., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.018 · Cited by 462 · see citing works
2016 IEEE International Conference on Big Data (Big Data)
(2016) · doi:10.1109/bigdata38862.2016 · Cited by 456 · see citing works
Blockchain Technology Use Cases in Healthcare
Zhang, P., Schmidt, D. C., White, J., & Lenz, G. (2018) · Advances in Computers · doi:10.1016/bs.adcom.2018.03.006 · Cited by 441 · see citing works
Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework
Chawla, N. V., & Davis, D. A. (2013) · Journal of General Internal Medicine · doi:10.1007/s11606-013-2455-8 · Cited by 440 · see citing works
Exploring the path to big data analytics success in healthcare
Wang, Y., & Hajli, N. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.002 · Cited by 429 · see citing works
Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic
Javaid, M., & Khan, I. H. (2021) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2021.01.015 · Cited by 423 · see citing works
Self-citationA Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective
Habibzadeh, H., Dinesh, K., Shishvan, O. R., Boggio-Dandry, A., Gaurav Sharma, A., & Soyata, T. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2946359 · Cited by 419 · see citing works
Delivering the power of nanomedicine to patients today
Germain, M., Caputo, F., Metcalfe, S. M., Tosi, G., Spring, K., Åslund, A., Pottier, A., Schiffelers, R. M., Ceccaldi, A., & Schmid, R. (2020) · Journal of Controlled Release · doi:10.1016/j.jconrel.2020.07.007 · Cited by 411 · see citing works
Advanced internet of things for personalised healthcare systems: A survey
Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2017.06.018 · Cited by 406 · see citing works
Khor, W. S., Baker, B., Amin, K., Chan, A. D. C., Patel, K. M., & Wong, J. K. F. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.12.23 · Cited by 398 · see citing works
Fernandes, H. R., Gaddam, A., Rebelo, A., Brazete, D., Stan, G. E., & Ferreira, J. M. F. (2018) · Materials · doi:10.3390/ma11122530 · Cited by 397 · see citing works
Liu, Y., Wang, H., Zhao, W., Zhang, M., Qin, H., & Xie, Y. (2018) · Sensors · doi:10.3390/s18020645 · Cited by 392 · see citing works
An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2843169 · Cited by 392 · see citing works
Dhariwala, B., Hunt, E., & Boland, T. (2004) · Tissue Engineering · doi:10.1089/ten.2004.10.1316 · Cited by 390 · see citing works
The Future Digital Work Force: Robotic Process Automation (RPA)
Madakam, S., Holmukhe, R. M., & Jaiswal, D. K. (2019) · Journal of Information Systems and Technology Management · doi:10.4301/s1807-1775201916001 · Cited by 388 · see citing works
COVID-19 and telemedicine: Immediate action required for maintaining healthcare providers well-being
Moazzami, B., Razavi-Khorasani, N., Dooghaie Moghadam, A., Farokhi, E., & Rezaei, N. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104345 · Cited by 388 · see citing works
Tissue Engineering by Self-Assembly of Cells Printed into Topologically Defined Structures
Jakab, K., Norotte, C., Damon, B. J., Marga, F., Neagu, A., Besch-Williford, C. L., Kachurin, A. M., Church, K. H., Park, H., Mironov, V., et al. (2008) · Tissue Engineering Part A · doi:10.1089/tea.2007.0173 · Cited by 384 · see citing works
Rahmani, A.-M., Thanigaivelan, N. K., Gia, T. N., Granados, J., Negash, B., Liljeberg, P., & Tenhunen, H. (2015) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc.2015.7158084 · Cited by 377 · see citing works
Big Data Analytics in Medicine and Healthcare
Ristevski, B., & Chen, M. (2018) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2017-0030 · Cited by 376 · see citing works
Internet of Things: A survey of enabling technologies in healthcare and its applications
Dhanvijay, M. M., & Patil, S. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.03.006 · Cited by 374 · see citing works
Bancroft, G. N., Sikavitsas, V. I., & Mikos, A. G. (2003) · Tissue Engineering · doi:10.1089/107632703322066723 · Cited by 372 · see citing works
Review on Wearable Technology Sensors Used in Consumer Sport Applications
Aroganam, G., Manivannan, N., & Harrison, D. (2019) · Sensors · doi:10.3390/s19091983 · Cited by 370 · see citing works
Axes of a revolution: challenges and promises of big data in healthcare
Shilo, S., Rossman, H., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0727-5 · Cited by 370 · see citing works
Will artificial intelligence solve the human resource crisis in healthcare?
Meskó, B., Hetényi, G., & Győrffy, Z. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3359-4 · Cited by 360 · see citing works
5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions
Ahad, A., Tahir, M., & Yau, K.-L. A. (2019) · IEEE Access · doi:10.1109/access.2019.2930628 · Cited by 352 · see citing works
Big Data, Big Knowledge: Big Data for Personalized Healthcare
Viceconti, M., Hunter, P. J., & Hose, R. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2406883 · Cited by 348 · see citing works
5G Communication: An Overview of Vehicle-to-Everything, Drones, and Healthcare Use-Cases
Ullah, H., Nair, N. G., Moore, A., Nugent, C., Muschamp, P., & Cuevas, M. (2019) · IEEE Access · doi:10.1109/access.2019.2905347 · Cited by 333 · see citing works
Bracq, M.-S., Michinov, E., & Jannin, P. (2019) · Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare · doi:10.1097/sih.0000000000000347 · Cited by 332 · see citing works
Recent Advances in Wearable Sensors for Health Monitoring
Rodgers, M. M., Pai, V. M., & Conroy, R. S. (2015) · IEEE Sensors Journal · doi:10.1109/jsen.2014.2357257 · Cited by 325 · see citing works
Chunara, R., Zhao, Y., Chen, J., Lawrence, K. S., Testa, P. A., Nov, O., & Mann, D. M. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa217 · Cited by 325 · see citing works
Data Mining in Healthcare – A Review
Jothi, N., Rashid, N. A., & Husain, W. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.145 · Cited by 305 · see citing works
A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes
Kumar, A., Krishnamurthi, R., Nayyar, A., Sharma, K., Grover, V., & Hossain, E. (2020) · IEEE Access · doi:10.1109/access.2020.3004790 · Cited by 302 · see citing works
Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02204-y · Cited by 294 · see citing works
Kim, S., Lee, K., Hwang, H. Y., & Yoo, S. (2015) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-016-0249-8 · Cited by 290 · see citing works
Edge cognitive computing based smart healthcare system
Chen, M., Li, W., Hao, Y., Qian, Y., & Humar, I. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.054 · Cited by 290 · see citing works
Current advances in electrospun gelatin-based scaffolds for tissue engineering applications
Aldana, A. A., & Abraham, G. A. (2016) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2016.09.044 · Cited by 286 · see citing works
Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study
Abdullah, R., & Fakieh, B. (2020) · Journal of Medical Internet Research · doi:10.2196/17620 · Cited by 286 · see citing works
IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art
Albahri, A. S., Alwan, J. K., Taha, Z. K., Ismail, S. F., Hamid, R. A., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., Alamoodi, A. H., & Alsalem, M. A. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102873 · Cited by 285 · see citing works
Ahmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2018) · Universal Access in the Information Society · doi:10.1007/s10209-018-0618-4 · Cited by 281 · see citing works
2012 IEEE International Conference on Communications (ICC)
(2012) · doi:10.1109/icc16399.2012 · Cited by 276 · see citing works
A Survey of Blockchain-Based Strategies for Healthcare
de Aguiar, E. J., Faiçal, B. S., Krishnamachari, B., & Ueyama, J. (2020) · ACM Computing Surveys · doi:10.1145/3376915 · Cited by 275 · see citing works
Emotion-aware connected healthcare big data towards 5G
Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273 · see citing works
Incorporating telemedicine as part of COVID-19 outbreak response systems
(2020) · The American Journal of Managed Care · doi:10.37765/ajmc.2020.42784 · Cited by 272 · see citing works
3D scanning applications in medical field: A literature-based review
Haleem, A., & Javaid, M. (2018) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2018.05.006 · Cited by 268 · see citing works
Self-citationInternet of Things and Big Data Technologies for Next Generation Healthcare
Bhatt, C., Dey, N., & Ashour, A. S. (2017) · Studies in big data · doi:10.1007/978-3-319-49736-5 · Cited by 260 · see citing works
Weaver, K. E., Rowland, J. H., Bellizzi, K. M., & Aziz, N. M. (2010) · Cancer · doi:10.1002/cncr.25209 · Cited by 254 · see citing works
Industry 4.0 applications in medical field: A brief review
Javaid, M., & Haleem, A. (2019) · Current Medicine Research and Practice · doi:10.1016/j.cmrp.2019.04.001 · Cited by 252 · see citing works
Self-citationIndustry 5.0: Potential Applications in COVID-19
Javaid, M., Haleem, A., Singh, R. P., Haq, M. I. U., Raina, A., & Suman, R. (2020) · Journal of Industrial Integration and Management · doi:10.1142/s2424862220500220 · Cited by 250 · see citing works
Self-citationSecure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0
Qiu, H., Meikang Qiu, A., Liu, M., & Memmi, G. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2973467 · Cited by 245 · see citing works
Singh, R. P., Javaid, M., Haleem, A., Vaishya, R., & Ali, S. (2020) · Journal of Clinical Orthopaedics and Trauma · doi:10.1016/j.jcot.2020.05.011 · Cited by 244 · see citing works
Self-citationPotentiality of Big Data in the Medical Sector: Focus on How to Reshape the Healthcare System
Jee, K., & Kim, G.-H. (2013) · Healthcare Informatics Research · doi:10.4258/hir.2013.19.2.79 · Cited by 242 · see citing works
Edge computing for Internet of Things: a survey, e-healthcare case study and future direction
Ray, P. P., Dash, D., & De, D. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.05.005 · Cited by 241 · see citing works
Lončar-Turukalo, T., Zdravevski, E., da Silva, J. M., Chouvarda, I., & Trajkovik, V. (2019) · Journal of Medical Internet Research · doi:10.2196/14017 · Cited by 240 · see citing works
Healthcare 4.0: trends, challenges and research directions
Tortorella, G. L., Fogliatto, F. S., Vergara, A., Vassolo, R. S., & Sawhney, R. S. (2019) · Production Planning & Control · doi:10.1080/09537287.2019.1702226 · Cited by 239 · see citing works
Health 4.0: On the Way to Realizing the Healthcare of the Future
Al‐Jaroodi, J., Mohamed, N., & AbuKhousa, E. (2020) · IEEE Access · doi:10.1109/access.2020.3038858 · Cited by 238 · see citing works
Drone-Aided Healthcare Services for Patients with Chronic Diseases in Rural Areas
Kim, S., Lim, G. J., Cho, J., & Côté, M. J. (2017) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-017-0548-z · Cited by 235 · see citing works
Who Owns the Data? Open Data for Healthcare
Kostkova, P., Brewer, H., de Lusignan, S., Fottrell, E., Goldacre, B., Hart, G., Koczan, P., Knight, P., Marsolier, C., McKendry, R. A., et al. (2016) · Frontiers in Public Health · doi:10.3389/fpubh.2016.00007 · Cited by 230 · see citing works
Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System
Kishor, A., & Chakraborty, C. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-08708-5 · Cited by 228 · see citing works
Flexible potentiometric pH sensors for wearable systems
Manjakkal, L., Dervin, S., & Dahiya, R. (2020) · RSC Advances · doi:10.1039/d0ra00016g · Cited by 224 · see citing works
Telemedicine and Advances in Urban and Rural Healthcare Delivery in Africa
Mars, M. (2013) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2013.10.006 · Cited by 223 · see citing works
Sustainable Wearables: Wearable Technology for Enhancing the Quality of Human Life
Lee, J., Kim, D., Ryoo, H.-Y., & Shin, B.-S. (2016) · Sustainability · doi:10.3390/su8050466 · Cited by 219 · see citing works
The Internet of Things in Healthcare: Potential Applications and Challenges
Laplante, P. A., & Laplante, N. (2016) · IT Professional · doi:10.1109/mitp.2016.42 · Cited by 219 · see citing works
How 5g wireless (and concomitant technologies) will revolutionize healthcare?
Latif, S., Qadir, J., Farooq, S., & Imran, M. (2017) · Future Internet · doi:10.3390/fi9040093 · Cited by 219 · see citing works
Big Data Security and Privacy Issues in Healthcare
Patil, H. K., & Seshadri, R. (2014) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdata.congress.2014.112 · Cited by 214 · see citing works
Homecare robotic systems for healthcare 4.0: visions and enabling technologies
Yang, G., Pang, Z., Jamal Deen, M., Dong, M., Zhang, Y.-T., Lovell, N., & Rahmani, A. M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2990529 · Cited by 213 · see citing works
New development: ‘Healing at a distance’—telemedicine and COVID-19
Leite, H., Hodgkinson, I. R., & Gruber, T. (2020) · Public Money & Management · doi:10.1080/09540962.2020.1748855 · Cited by 212 · see citing works
Organic Transistor-Based Chemical Sensors for Wearable Bioelectronics
Lee, M. Y., Lee, H. R., Park, C. H., Han, S. G., & Oh, J. H. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00465 · Cited by 211 · see citing works
Toward a Literature-Driven Definition of Big Data in Healthcare
Baro, E., Degoul, S., Beuscart, R., & Chazard, E. (2015) · BioMed Research International · doi:10.1155/2015/639021 · Cited by 210 · see citing works
An Edge Computing Based Smart Healthcare Framework for Resource Management
Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., & Baker, T. (2018) · Sensors · doi:10.3390/s18124307 · Cited by 210 · see citing works
A novel blockchain-based electronic health record automation system for healthcare
Chelladurai, U., & Pandian, S. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03163-3 · Cited by 210 · see citing works
Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01650-z · Cited by 207 · see citing works
A Secured Framework for SDN-Based Edge Computing in IoT-Enabled Healthcare System
Li, J., Jinjin, C., Khan, F., Rehman, A. U., Balasubramaniam, V., Sun, J., & Venu, P. (2020) · IEEE Access · doi:10.1109/access.2020.3011503 · Cited by 205 · see citing works
Pustokhina, I. V., Pustokhin, D. A., Gupta, D. K., Khanna, A. K., Shankar, K., & Nguyen, G. N. (2020) · IEEE Access · doi:10.1109/access.2020.3000322 · Cited by 204 · see citing works
Adopting the Internet of Things technologies in health care systems
Chiuchisan, I., Costin, H. N., & Geman, O. (2014) · International Conference and Exposition on Electrical and Power Engineering (EPE) · doi:10.1109/icepe.2014.6969965 · Cited by 202 · see citing works
The role of 5G for digital healthcare against COVID-19 pandemic: Opportunities and challenges
Siriwardhana, Y., Gür, G., Ylianttila, M., & Liyanage, M. (2020) · ICT Express · doi:10.1016/j.icte.2020.10.002 · Cited by 197 · see citing works
Securing electronics healthcare records in Healthcare 4.0 : A biometric-based approach
Hathaliya, J. J., Tanwar, S., Tyagi, S., & Kumar, N. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.04.017 · Cited by 194 · see citing works
A comprehensive review on usage of Internet of Things (IoT) in healthcare system
Darshan, K. R., & Anandakumar, K. (2015) · International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) · doi:10.1109/erect.2015.7499001 · Cited by 193 · see citing works
Galle, A., Semaan, A. T., Huysmans, E., Audet, C., Asefa, A., Delvaux, T., Afolabi, B. B., Ayadi, A. M. E., & Beňová, L. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2020-004575 · Cited by 193 · see citing works
3D body scanning and healthcare applications
Treleaven, P., & Wells, J. (2007) · Computer · doi:10.1109/mc.2007.225 · Cited by 192 · see citing works
Li, D. (2019) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbz020 · Cited by 191 · see citing works
Healthcare 4.0: A review of frontiers in digital health
Jayaraman, P. P., Forkan, A. R. M., Morshed, A., Haghighi, P. D., & Kang, Y. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1350 · Cited by 191 · see citing works
Drones in medicine-The rise of the machines
Balasingam, M. (2017) · International Journal of Clinical Practice · doi:10.1111/ijcp.12989 · Cited by 190 · see citing works
A review of wearable technology in medicine
Iqbal, M. H., Aydın, A., Brunckhorst, O., Prokar Dasgupta, A., & Ahmed, K. (2016) · Journal of the Royal Society of Medicine · doi:10.1177/0141076816663560 · Cited by 189 · see citing works
User experiences of virtual reality technologies for healthcare in learning: an integrative review
Mäkinen, H., Haavisto, E., Havola, S., & Koivisto, J. (2020) · Behaviour and Information Technology · doi:10.1080/0144929x.2020.1788162 · Cited by 184 · see citing works
Mobile edge computing based QoS optimization in medical healthcare applications
Sodhro, A. H., Luo, Z., Sangaiah, A. K., & Baik, S. W. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.004 · Cited by 183 · see citing works
Uddin, M. Z. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.08.010 · Cited by 182 · see citing works
Stimuli‐Responsive Delivery of Growth Factors for Tissue Engineering
Qu, M., Jiang, X., Zhou, X., Wang, C., Wu, Q., Ren, L. M., Zhu, J., Zhu, S., Tebon, P. J., Sun, W., et al. (2020) · Advanced Healthcare Materials · doi:10.1002/adhm.201901714 · Cited by 178 · see citing works
The International Conference on Advanced Machine Learning Technologies and Applications
Hassanien, A. E., & all, E. (2020) · Cited by 175 · see citing works
Recent developments in sensors for wearable device applications
Cheng, Y., Wang, K., Xu, H., Li, T., Jin, Q., & Cui, D. (2021) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-021-03602-2 · Cited by 175 · see citing works
Virtual Reality Goes to War: A Brief Review of the Future of Military Behavioral Healthcare
Rizzo, A., Parsons, T. D., Lange, B. S., Kenny, P. G., Buckwalter, J. G., ROTHBAUM, B. O., Difede, J., Frazier, J. R., Newman, B., Williams, J., et al. (2011) · Journal of Clinical Psychology in Medical Settings · doi:10.1007/s10880-011-9247-2 · Cited by 174 · see citing works
IoT-based smart healthcare video surveillance system using edge computing
Rajavel, R., Ravichandran, S. K., Harimoorthy, K., Nagappan, P., & Gobichettipalayam, K. R. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03157-1 · Cited by 174 · see citing works
Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance
Guan, J. (2019) · Chinese Medical Sciences Journal · doi:10.24920/003611 · Cited by 170 · see citing works
Blockchain Based Smart Contracts for Internet of Medical Things in e-Healthcare
Sharma, A., Sarishma, Tomar, R., Chilamkurti, N., & Kim, B. (2020) · Electronics · doi:10.3390/electronics9101609 · Cited by 170 · see citing works
Robotic Process Automation: Systematic Literature Review
Ivančić, L., Vugec, D. S., & Vukšić, V. B. (2019) · Lecture notes in business information processing · doi:10.1007/978-3-030-30429-4_19 · Cited by 164 · see citing works
Schuemie, M. J., Hripcsak, G., Ryan, P. B., Madigan, D., & Suchard, M. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708282114 · Cited by 163 · see citing works
Smart Healthcare in the Era of Internet-of-Things
Zhu, H., Wu, C. K., Koo, C. H., Tsang, Y. T., Liu, Y., Chi, H. R., & Tsang, K. F. (2019) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2019.2923929 · Cited by 162 · see citing works
Future of additive manufacturing in healthcare
Ghomi, E. R., Khosravi, F., Neisiany, R. E., Singh, S., & Ramakrishna, S. (2020) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2020.100255 · Cited by 162 · see citing works
A 5G Cognitive System for Healthcare
Chen, M., Yang, J., Hao, Y., Mao, S., & Hwang, K. (2017) · Big Data and Cognitive Computing · doi:10.3390/bdcc1010002 · Cited by 159 · see citing works
Developing a delivery science for artificial intelligence in healthcare
Li, R. C., Asch, S., & Shah, N. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00318-y · Cited by 159 · see citing works
Artificial intelligence, machine learning and the evolution of healthcare
Jones, L. D., Golan, D., Hanna, S. A., & Ramachandran, M. (2018) · Bone and Joint Research · doi:10.1302/2046-3758.73.bjr-2017-0147.r1 · Cited by 158 · see citing works
Damoah, I. S., Ayakwah, A., & Tingbani, I. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129598 · Cited by 157 · see citing works
Pang, Z., Yang, G., Khédri, R., & Zhang, Y. (2018) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2848518 · Cited by 156 · see citing works
2017 IEEE 85th Vehicular Technology Conference (VTC Spring)
(2017) · doi:10.1109/vtc-spring39600.2017 · Cited by 156 · see citing works
The Role of the Internet of Things in Healthcare: Future Trends and Challenges
Aghdam, Z. N., Rahmani, A. M., & Hosseinzadeh, M. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105903 · Cited by 153 · see citing works
Big Data Security Intelligence for Healthcare Industry 4.0
Manogaran, G., Thota, C., Lopez, D., & Sundarasekar, R. (2017) · Springer series in advanced manufacturing · doi:10.1007/978-3-319-50660-9_5 · Cited by 151 · see citing works
Impacts of Healthcare 4.0 digital technologies on the resilience of hospitals
Tortorella, G. L., Saurin, T. A., Fogliatto, F. S., da Rosa, V. M., Tonetto, L. M., & Magrabi, F. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120666 · Cited by 148 · see citing works
2018 IEEE Globecom Workshops (GC Wkshps)
(2018) · doi:10.1109/gcwkshps43968.2018 · Cited by 146 · see citing works
Sreedevi, A. G., Harshitha, T., Sugumaran, V., & Shankar, P. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.102888 · Cited by 145 · see citing works
Griffiths, P., Ball, J., Murrells, T., Jones, S., & Rafferty, A. M. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-008751 · Cited by 141 · see citing works
Big Data in Healthcare Management: A Review of Literature
Senthilkumar, S. A. (2018) · American Journal of Theoretical and Applied Business · doi:10.11648/j.ajtab.20180402.14 · Cited by 139 · see citing works
2015 IEEE International Congress on Big Data
(2015) · doi:10.1109/bigdatacongress35851.2015 · Cited by 139 · see citing works
Edge Computing with Cloud for Voice Disorder Assessment and Treatment
Muhammad, G., Alhamid, M. F., Alsulaiman, M., & Gupta, B. B. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700790 · Cited by 137 · see citing works
Telemedicine, Privacy, and Information Security in the Age of COVID-19
Jalali, M. S., Landman, A., & Gordon, W. J. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa310 · Cited by 137 · see citing works
2016 Future Technologies Conference (FTC)
(2016) · doi:10.1109/ftc38997.2016 · Cited by 137 · see citing works
Smart Medical Information Technology for Healthcare (SMITH)
Winter, A., Stäubert, S., Ammon, D., Aiche, S., Beyan, O., Bischoff, V., Daumke, P., Decker, S., Funkat, G., Gewehr, J. E., et al. (2018) · Methods of Information in Medicine · doi:10.3414/me18-02-0004 · Cited by 135 · see citing works
Intricacies of medical drones in healthcare delivery: Implications for Africa
Nyaaba, A. A., & Ayamga, M. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101624 · Cited by 135 · see citing works
A Discussion of Virtual Reality As a New Tool for Training Healthcare Professionals
Fertleman, C. R., Aubugeau-Williams, P., Sher, C., Lim, A.-N., Lumley, S., Delacroix, S., & Pan, X. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00044 · Cited by 132 · see citing works
Volume and value of big healthcare data
Dinov, I. D. (2016) · Journal of Medical Statistics and Informatics · doi:10.7243/2053-7662-4-3 · Cited by 130 · see citing works
Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives
Miron‐Shatz, T., Lau, A. Y. S., Paton, C., & Hansen, M. M. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0004 · Cited by 127 · see citing works
Hiebert, B., Nouvet, É., Jeyabalan, V., & Donelle, L. (2020) · Drones · doi:10.3390/drones4030030 · Cited by 127 · see citing works
Big data stream computing in healthcare real-time analytics
Ta, V.-D., Liu, C.-M., & Nkabinde, G. W. (2016) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda.2016.7529531 · Cited by 125 · see citing works
Governing the safety of artificial intelligence in healthcare
Macrae, C. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2019-009484 · Cited by 123 · see citing works
Building Caring Healthcare Systems in the Internet of Things
Laplante, P. A., Kassab, M., Laplante, N. L., & Voas, J. M. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2017.2662602 · Cited by 121 · see citing works
Healthcare Associated Infections—A New Pathology in Medical Practice?
Voidăzan, S. T., Albu, S., Toth, R., Grigorescu, B. L., Răchită, A. I. C., & Moldovan, I. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17030760 · Cited by 121 · see citing works
Poljak, M., & Šterbenc, A. (2019) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2019.09.014 · Cited by 119 · see citing works
Modelling the barriers of Health 4.0–the fourth healthcare industrial revolution in India by TISM
Ajmera, P., & Jain, V. (2019) · Operations Management Research · doi:10.1007/s12063-019-00143-x · Cited by 118 · see citing works
Cost–Effectiveness of Nanomedicine: Estimating the Real Size of Nano-Costs
Bosetti, R., & Jones, S. L. (2019) · Nanomedicine · doi:10.2217/nnm-2019-0130 · Cited by 117 · see citing works
Nanomedicine: promises and challenges for the future of public health
Brenner, S. (2010) · International Journal of Nanomedicine · doi:10.2147/ijn.s13816 · Cited by 114 · see citing works
The Times they Are a-Changin’ – Healthcare 4.0 Is Coming!
Chen, C., Loh, E., Kuo, K. N., & Tam, K. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1513-0 · Cited by 114 · see citing works
Yurt, A., Çavuşoğlu, B., & Günay, T. (2014) · Molecular Imaging and Radionuclide Therapy · doi:10.4274/mirt.00719 · Cited by 112 · see citing works
Zechariah, S., Ansa, B. E., Johnson, S. W., Gates, A., & Leo, G. D. (2019) · Healthcare · doi:10.3390/healthcare7040117 · Cited by 112 · see citing works
Telemedicine technologies for confronting COVID-19 pandemic: a review
Bahl, S., Singh, R. P., Javaid, M., Khan, I. H., Vaishya, R., & Suman, R. (2020) · Journal of Industrial Integration and Management · doi:10.1142/s2424862220300057 · Cited by 112 · see citing works
Self-citationBlockchain-Based UAV Path Planning for Healthcare 4.0: Current Challenges and the Way Ahead
Aggarwal, S., Kumar, N., Alhussein, M. A., & Muhammad, G. (2021) · IEEE Network · doi:10.1109/mnet.011.2000069 · Cited by 112 · see citing works
Towards a Process Analysis Approach to Adopt Robotic Process Automation
Leshob, A., Bourgouin, A., & Renard, L. (2018) · IEEE International Conference on e-Business Engineering (ICEBE) · doi:10.1109/icebe.2018.00018 · Cited by 112 · see citing works
Health 4.0: Applications, Management, Technologies and Review
Khelassi, A., Estrela, V. V., Monteiro, A. C. B., França, R. P., Iano, Y., & Razmjooy, N. (2019) · Medical Technologies Journal · doi:10.26415/2572-004x-vol2iss4p262-276 · Cited by 109 · see citing works
EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems
Li, X., Huang, X., Li, C., Yu, R., & Shu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2898265 · Cited by 109 · see citing works
Muddling through cybersecurity: Insights from the U.S. healthcare industry
Abraham, C., Chatterjee, D., & Sims, R. R. (2019) · Business Horizons · doi:10.1016/j.bushor.2019.03.010 · Cited by 108 · see citing works
Towards Medical Data Interoperability Through Collaboration of Healthcare Devices
Jaleel, A., Mahmood, T., Awais, M., Bano, G., & Khurshid, S. K. (2020) · IEEE Access · doi:10.1109/access.2020.3009783 · Cited by 108 · see citing works
Hampshire, A., Hellyer, P. J., Soreq, E., Mehta, M. A., Ioannidis, K., Trender, W. R., Grant, J. E., & Chamberlain, S. R. (2021) · Nature Communications · doi:10.1038/s41467-021-24365-5 · Cited by 108 · see citing works
Flagged first, then by citations
28 of the 411 references couldn’t be matched to a known paper.
Research works printed but not linked: 8
- Wu, 2019, Wearable technology applications in healthcare: a literature review, Online J. Nurs. Inform., 23
- Kulkarni, 2014, Healthcare applications of the internet of things: a review, Int. J. Comput. Sci. Inf. Technol., 5, 6229
- Azzawi, 2016, A review on internet of things (IoT) in healthcare, Int. J. Appl. Eng. Res., 11, 10216
- Srivastava, 2018, Self-organised design of virtual reality simulator for identification and optimization of healthcare software components, J. Ambient Intell. Hum. Comput., 1
- Kumar, 2018, Healthcare solution based on machine learning applications in IOT and edge computing, Int. J. Pure Appl. Math., 119, 1473
- Ransikabum, 2019, Additive manufacturing-based healthcare 3D model for education: literature review and A feasibility study, 1
- Pathak, 2019, Humanitarian impact of drones in healthcare and disaster management, Int. J. Recent Technol. Eng., 7, 201
- Tanwar, 2020, Blockchain-based electronic healthcare record system for healthcare 4.0 applications, J. Inf. Secur. Appl., 50
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Self-citationAla, A., Šimić, V., Pamucar, D. S., & Bačanin, N. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107889 · Cited by 67 · see citing works
Qoseem, I. O., Okesanya, O. J., Olaleke, N. O., Ukoaka, B. M., Amisu, B. O., Ogaya, J. B., & Lucero‐Prisno, D. E. (2024) · Health Promotion Perspectives · doi:10.34172/hpp.42822 · Cited by 66 · see citing works
Risk Assessment of Heterogeneous IoMT Devices: A Review
Pritika, P., Shanmugam, B., & Azam, S. (2023) · Technologies · doi:10.3390/technologies11010031 · Cited by 64 · see citing works
Technical and regulatory challenges of digital health implementation in developing countries
Meslamani, A. Z. A. (2023) · Journal of Medical Economics · doi:10.1080/13696998.2023.2249757 · Cited by 64 · see citing works
Intelligent Digital Twins for Personalized Migraine Care
Gazerani, P. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13081255 · Cited by 61 · see citing works
ARTIFICIAL INTELLIGENCE IN HEALTHCARE: A REVIEW OF ETHICAL DILEMMAS AND PRACTICAL APPLICATIONS
Anyanwu, E. C., Okongwu, C. C., Olorunsogo, T. O., Ayo-Farai, O., Osasona, F., & Daraojimba, O. D. (2024) · International Medical Science Research Journal · doi:10.51594/imsrj.v4i2.755 · Cited by 54 · see citing works
Aljohani, A. A. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00568-8 · Cited by 45 · see citing works
5G Technology in the Digital Transformation of Healthcare, a Systematic Review
Cabanillas-Carbonell, M., Martínez, J. E. P., & Yáñez, J. A. (2023) · Sustainability · doi:10.3390/su15043178 · Cited by 43 · see citing works
Ali, G., & Mijwil, M. M. (2024) · Mesopotamian Journal of CyberSecurity · doi:10.58496/mjcs/2024/006 · Cited by 42 · see citing works
Ranjan, R., & Sahana, B. C. (2024) · Engineered Science · doi:10.30919/es1175 · Cited by 42 · see citing works
Additive manufacturing in biomedical: Applications, challenges, and prospects
Mobarak, M. H., Abid, A. S., Munna, M. S., Dutta, M., & Rimon, M. I. H. (2025) · Hybrid Advances · doi:10.1016/j.hybadv.2025.100467 · Cited by 42 · see citing works
Health apps and patient engagement: A review of effectiveness and user experience
Omaghomi, T. T., Elufioye, O. A., Akomolafe, O. O., Anyanwu, E. C., & Daraojimba, A. I. (2023) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.2.0476 · Cited by 40 · see citing works
Empirical nexus of hospital brand gestalt, patient satisfaction and revisit intention
Mandagi, D. W., Rampen, D. C., Soewignyo, T. I., & Walean, R. H. (2023) · International Journal of Pharmaceutical and Healthcare Marketing · doi:10.1108/ijphm-04-2023-0030 · Cited by 37 · see citing works
Kumi, M., Wang, T., Ejeromedoghene, O., Wang, J., Li, P., & Huang, W. (2024) · Small Methods · doi:10.1002/smtd.202301341 · Cited by 37 · see citing works
Exploring Sustainability and Efficiency Improvements in Healthcare: A Qualitative Study
Vasileiou, A., Sfakianaki, E., & Tsekouropoulos, G. (2024) · Sustainability · doi:10.3390/su16198306 · Cited by 37 · see citing works
Critical review of self‐diagnosis of mental health conditions using artificial intelligence
Wimbarti, S., Kairupan, B. H. R., & Tallei, T. E. (2024) · International Journal of Mental Health Nursing · doi:10.1111/inm.13303 · Cited by 35 · see citing works
Adeghe, E. P., Okolo, C. A., & Ojeyinka, O. T. (2024) · International Journal of Science and Technology Research Archive · doi:10.53771/ijstra.2024.6.1.0032 · Cited by 35 · see citing works
Alkish, I., Iyiola, K., Alzubi, A. B., & Aljuhmani, H. Y. (2025) · Sustainability · doi:10.3390/su17104365 · Cited by 35 · see citing works
Challenges and opportunities for enhanced patient care with mobile robots in healthcare
Sahoo, S. K., & Choudhury, B. B. (2023) · Journal of Mechatronics and Artificial Intelligence in Engineering · doi:10.21595/jmai.2023.23410 · Cited by 34 · see citing works
Soft computing techniques for biomedical data analysis: open issues and challenges
Houssein, E. H., Hosney, M. E., Emam, M. M., Younis, E. M. G., Ali, A. A., & Mohamed, W. M. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10585-2 · Cited by 34 · see citing works
Machine Learning-based maternal health risk prediction model for IoMT framework
Mondal, S., Nag, A., Barman, A. K., & Karmakar, M. (2023) · International Journal of experimental research and review · doi:10.52756/ijerr.2023.v32.012 · Cited by 33 · see citing works
Guarding Digital Health: Deep Learning for Attack Detection in Medical IoT
Vaisakhkrishnan, K., Ashok, G., Mishra, P., & Kumar, T. G. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.235 · Cited by 33 · see citing works
Sharma, V., Jamwal, A., Agrawal, R., & Pratap, S. (2024) · Waste Management & Research The Journal for a Sustainable Circular Economy · doi:10.1177/0734242x241285420 · Cited by 33 · see citing works
Measuring Digital Health Literacy in Older Adults: Development and Validation Study
Kim, S. I., Park, C., Park, S., Kim, D., Bae, Y. S., Kang, J., & Chun, J. (2025) · Journal of Medical Internet Research · doi:10.2196/65492 · Cited by 31 · see citing works
Abu-Jassar, A. T., Attar, H. H., Amer, A., Lyashenko, V. V., Yevsieiev, V., & Solyman, A. (2025) · International Journal of Crowd Science · doi:10.26599/ijcs.2023.9100019 · Cited by 31 · see citing works
Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations
McGrath, C. P. J., Chau, R. C. W., & Molina, G. F. (2025) · Frontiers in Oral Health · doi:10.3389/froh.2025.1587630 · Cited by 30 · see citing works
Artificial Intelligence in Healthcare: Current Regulatory Landscape and Future Directions
Ardıç, N., & Dinç, R. (2025) · British Journal of Hospital Medicine · doi:10.12968/hmed.2024.0972 · Cited by 30 · see citing works
Internet of Things (IoT) in healthcare: A systematic review of use cases and benefits
Atadoga, A., Omaghomi, T. T., Elufioye, O. A., Odilibe, I. P., Daraojimba, A. I., & Owolabi, O. R. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.11.1.0243 · Cited by 30 · see citing works
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2022) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2023.100085 · Cited by 29 · see citing works
Self-citationArtificial Intelligence Solutions for Health 4.0: Overcoming Challenges and Surveying Applications
Al-Mistarehi, A.-H., Mijwil, M. M., Filali, Y., Bounabi, M., Ali, G., & Abotaleb, M. (2023) · Mesopotamian Journal of Artificial Intelligence in Healthcare · doi:10.58496/mjaih/2023/003 · Cited by 29 · see citing works
Engineering with Biomedical Sciences Changing the Horizon of Healthcare-A Review
Weerarathna, I. N., Kumar, P., Luharia, A. A., & Mishra, G. (2024) · Bioengineered · doi:10.1080/21655979.2024.2401269 · Cited by 27 · see citing works
Cybersecurity in Healthcare: New Threat to Patient Safety
Aldosari, B. (2025) · Cureus · doi:10.7759/cureus.83614 · Cited by 27 · see citing works
Enhancing healthcare data privacy and interoperability with federated learning
Akhmetov, A., Latif, Z., Tyler, B., & Yazıcı, A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2870 · Cited by 27 · see citing works
Ayena, J. C., Bouayed, A., Arous, M. B., Ouakrim, Y., Loulou, K., Ameyed, D., Savard, I., Kamel, L. E., & Mezghani, N. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1581285 · Cited by 27 · see citing works
Sigawi, T., & Ilan, Y. (2023) · Biomimetics · doi:10.3390/biomimetics8040359 · Cited by 27 · see citing works
Leveraging AI and NLP in Chatbot Development: An Experimental Study
Paracha, A. W., Arshad, U., Ali, R. H., Abideen, Z. U., Shah, M. H., Khan, T. A., Ijaz, A. Z., Ali, N., & Siddique, A. B. (2023) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit60620.2023.00040 · Cited by 26 · see citing works
Ametefe, D. S., Sarnin, S. S., Ali, D. M., Muhamad, W. N. W., Ametefe, G. D., John, D., & Aliu, A. A. (2024) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-024-01066-3 · Cited by 26 · see citing works
Optimizing healthcare big data performance through regional computing
Alsahfi, T., Badshah, A., Aboulola, O. I., & Daud, A. Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-87515-5 · Cited by 26 · see citing works
Intelligent information management enables quality-by-design in pharmaceutical production
Zhu, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-27879-w · Cited by 26 · see citing works
Sreejith, R., & Senthil, S. T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15180 · Cited by 26 · see citing works
Beyond Healing: Embracing the Triple Bottom Line Approach in Post-pandemic Healthcare
Alim, M., & Sulley, S. (2024) · Cureus · doi:10.7759/cureus.54019 · Cited by 25 · see citing works
Khan, S., Saied, I., Ratnarajah, T., & Arslan, T. (2022) · Sensors · doi:10.3390/s22218519 · Cited by 25 · see citing works
The Paradoxes of Digital Tools in Hospitals: Qualitative Interview Study
Wosny, M., Strasser, L. M., & Hastings, J. (2024) · Journal of Medical Internet Research · doi:10.2196/56095 · Cited by 24 · see citing works
Kang, Y., Peng, L., Guo, J. M., Lu, Y., Yang, Y., Fan, B., & Pu, B. (2023) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2023.3334373 · Cited by 24 · see citing works
Comprehensive review on Parenting and Technology: Opportunities and Challenges in the 21st Century
Kurt, I. (2023) · European Journal of Social Sciences Education and Research · doi:10.26417/610cpd97 · Cited by 24 · see citing works
Performance measurement systems in primary health care: a systematic literature review
SANTOS, C. J. D. M., Barbosa, A. S., & Sant’Anna, Â. M. O. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12412-6 · Cited by 23 · see citing works
Younesi, A., Oustad, E., Ansari, M., Fahringer, T., & Buyya, R. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101815 · Cited by 23 · see citing works
Wang, S., Wang, F., Qiao, S., Zhuang, Y., Zhang, K., Pang, S., Nowak, R. M., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3186534 · Cited by 23 · see citing works
Digital transformation, institutional quality and productivity in Sub-Saharan Africa
Egbeleo, E., & Sodokin, K. (2025) · Cogent Economics & Finance · doi:10.1080/23322039.2025.2519924 · Cited by 22 · see citing works
A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine
Zaitseva, E., Levashenko, V., Rabčan, J., & Kvaššay, M. (2023) · Bioengineering · doi:10.3390/bioengineering10070838 · Cited by 22 · see citing works
Revolutionizing Oncology: A Comprehensive Review of Digital Health Applications
Borkar, S., Chakole, S. V., Prasad, R., & Bansod, S. (2024) · Cureus · doi:10.7759/cureus.59203 · Cited by 21 · see citing works
A fatigue assessment method based on attention mechanism and surface electromyography
Dang, Y., Liu, Z., Yang, X., Ge, L., & Miao, S. J. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.03.002 · Cited by 20 · see citing works
Agrawal, V., Agrawal, S., Bomanwar, A., Dubey, T., & Jaiswal, A. (2023) · Cureus · doi:10.7759/cureus.48767 · Cited by 20 · see citing works
Ahmad, M., Javaid, M., & Haleem, A. (2024) · Biomedical Analysis · doi:10.1016/j.bioana.2024.02.001 · Cited by 20 · see citing works
Self-citationZainab, I., Naseem, Z., Batool, S. R., Waqas, M., Nazir, A., & Nazeer, M. A. (2025) · Beilstein Journal of Nanotechnology · doi:10.3762/bjnano.16.46 · Cited by 19 · see citing works
Pesqueira, A. M., Sousa, M. J., Pereira, R., & Schwendinger, M. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.022 · Cited by 19 · see citing works
Evaluating the Impact of Healthcare 4.0 on the Performance of Hospitals
Kumar, P., Maurya, N., Keerthiraj, Krishna, S. H., Manoharan, G., & Bharti, A. (2024) · Wiley eBooks · doi:10.1002/9781394197705.ch1 · Cited by 19 · see citing works
Hundal, G. S., Rhodes, D. H., & Laux, C. M. (2025) · Sustainability · doi:10.3390/su17135690 · Cited by 18 · see citing works
Falahan, S. N., Habibi, E., Kamyari, N., & Yousofvand, V. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05375-5 · Cited by 18 · see citing works
Sawant, S., Joshi, P., & Sawant, S. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231192409 · Cited by 17 · see citing works
A Novel Explainable Attention-Based Meta-Learning Framework for Imbalanced Brain Stroke Prediction
Abousaber, I. (2025) · Sensors · doi:10.3390/s25061739 · Cited by 17 · see citing works
Ekingen, E., Yıldırım, F., Bayar, Ö., Akbal, E., Sercek, I., Hafeez‐Baig, A., Doğan, Ş., & Tuncer, T. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01721-1 · Cited by 17 · see citing works
Kothandaraman, L., Balasubramanian, N. K., Palaniyappan, S., Dalaq, A. S., & Bodaghi, M. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.113707 · Cited by 17 · see citing works
Wearable IoT Devices in Rehabilitation
Aziz, R., Jawed, F., Khan, S. A., & Sundus, H. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-2105-8.ch018 · Cited by 17 · see citing works
Aritra, S., Shikha, C. B., & Indu, S. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.12.001 · Cited by 17 · see citing works
The Development, Characteristics, and Challenges of Biosensors: The Example of Blood Glucose Meters
Chen, H., & Chen, C. (2025) · Chemosensors · doi:10.3390/chemosensors13080300 · Cited by 16 · see citing works
The Nexus of Healthcare and Technology
Darda, P., & Matta, N. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-3661-8.ch013 · Cited by 16 · see citing works
Halmosi, P., & Aranyossy, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102620 · Cited by 16 · see citing works
Lucia, A. D., Perlini, C., Chiarotto, A., Pachera, S., Pasini, I., Piccolo, L. D., & Donisi, V. (2024) · Journal of Medical Internet Research · doi:10.2196/55366 · Cited by 16 · see citing works
Mezzalira, E., Canzan, F., Marini, G. M. F., Longhini, J., Leardini, C., Saiani, L., & Ambrosi, E. (2024) · Health Policy and Technology · doi:10.1016/j.hlpt.2024.100896 · Cited by 16 · see citing works
Ametefe, D. S., Sarnin, S. S., Ali, D. M., Ametefe, G. D., John, D., & Hussin, N. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10423-8 · Cited by 16 · see citing works
Landi, S., Maistri, G., Orsini, L. P., Leardini, C., Malandra, S., & Antonelli, A. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12598-9 · Cited by 16 · see citing works
Narratives of medical tourists travelling for hair transplant: a case of Turkiye
Tanrıverdi, H., Çıkı, K. D., Uygun, N., & Çıkı, S. (2025) · Tourism Recreation Research · doi:10.1080/02508281.2025.2471484 · Cited by 16 · see citing works
Shahid, M. K., Mahmood, K., Altayyar, R. S., & Abdullah, H. (2025) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-02-2025-0114 · Cited by 15 · see citing works
AI and Digital Twins Transforming Healthcare IoT
Sharma, V., Sharma, K., & Kumar, A. (2024) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence60223.2024.10463366 · Cited by 15 · see citing works
Almalawi, A., Zafar, A., Unhelkar, B., Hassan, S., Alqurashi, F. A., Khan, A. I., Fahad, A., & Alam, M. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e33792 · Cited by 15 · see citing works
Retracted
- Authors
- Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R.
- Affiliations
- Jamia Millia Islamia (IN)
- Govind Ballabh Pant University of Agriculture and Technology (IN)
- Corresponding author
- Javaid, Mohd
- Venue
- Internet of Things and Cyber-Physical Systems
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 2
- Pages
- 12-30
- ISSN
- 2667-3452 (ISSN-L)
- OpenAlex
- W4224055696
- Semantic Scholar
- 6aad7c44242e2148b0cac848aa933f33cd0d9467
- Cited by
- 468
- References
- 411
- Citations by year
- 2026: 53 · 2025: 211 · 2024: 119 · 2023: 76 · 2022: 8
- FWCI
- 102.48
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Internet of Things and Cyber-Physical Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- Fourth Industrial Revolution, Internet of Things, digitalization of healthcare, patient-centered care, cloud computing, mobile computing, patient information, health care access, new medical technologies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
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