The DOI matches its Crossref record; 60 of the 61 references it cites were found in a registry or an index; 31 of the 75 sources its claims cite were checked for support.
The rise of artificial intelligence in healthcare applications
Open accessAbstract
Big data and machine learning are having an impact on most aspects of modern life, from entertainment, commerce, and healthcare. Netflix knows which films and series people prefer to watch, Amazon knows which items people like to buy when and where, and Google knows which symptoms and conditions people are searching for. All this data can be used for very detailed personal profiling, which may be of great value for behavioral understanding and targeting but also has potential for predicting healthcare trends. There is great optimism that the application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. It is generally believed that AI tools will facilitate and enhance human work and not replace the work of physicians and other healthcare staff as such. AI is ready to support healthcare personnel with a variety of tasks from administrative workflow to clinical documentation and patient outreach as well as specialized support such as in image analysis, medical device automation, and patient monitoring. In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This book chapter reviews major applications of artificial intelligence (AI) across healthcare, from precision medicine and drug discovery to imaging, wearables, robotics, and ambient assisted living. It argues that AI will generally augment rather than replace clinicians, but that adoption will be slowed by regulatory, integration, validation, and workflow challenges.
Key findings
- The chapter reports that AI applications could cut annual US healthcare costs by USD 150 billion in 2026, and that the AI-associated healthcare market is expected to reach USD 6.6 billion by 2021 at a 40% compound annual growth rate.
- It highlights performance examples including laparoscopic video analysis with 92.8% accuracy in identifying all procedure steps, AtomNet achieving an AUC of 0.9 or more for 58% of targets, and MoleculeNet having tested more than 700,000 compounds for toxicity or other properties.
- It describes deep learning as using networks with more than 10 layers, compared with earlier neural networks of only 3–5 layers, and notes that OHDSI consolidated 1.26 billion patient records from 17 countries.
- It states that half of people above age 65 have a disability, over 35 million people in the United States alone, and that AI is expected to support tasks such as administrative workflow, clinical documentation, image analysis, medical device automation, and patient monitoring.
Methods
- Design: narrative book chapter/review, not a primary research study; it discusses selected applications of AI in healthcare and the healthcare value chain.
- Data or sample: no primary dataset or study sample is analyzed; the chapter cites published literature, company examples, and prior studies, including a VR mastoidectomy study with one group of n=18 surgical trainees.
- Main analysis: thematic discussion of precision medicine, genetics, drug discovery, medical visualization, AR/VR, personal health records, NLP, robotics, neuroprosthetics, ambient assisted living, and primary care AI.
Limitations
- Stated: the chapter notes that telehealth solutions still need substantial independent validation for patient safety and efficacy, that IBM Watson has yet to show clinical value to patients, that evidence for gamification efficacy is scarce, and that surveyed primary care practitioners were skeptical about AI’s future role.
- Stated: widespread adoption requires regulatory approval, EHR integration, standardization, clinician training, payer coverage, and field updates; the authors expect limited clinical use within 5 years and more extensive use within 10 years.
- Evident: as a narrative review, it does not describe a systematic search strategy, inclusion criteria, quality appraisal, or quantitative synthesis, and many cited examples are early-stage, pilot, or company-reported rather than definitive clinical evidence.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Verification
Claims the paper makes
32 No full text of the sources · 22 Own interpretation · 17 Supported · 12 Partly supported · 6 Not checked yet · 5 Sources not found · 1 Couldn’t confirm · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Telehealth technology is also relevant in developing countries where the healthcare system is expanding and where healthcare infrastructure can be designed to meet the current needs [3].”
§ The rise of artificial intelligence in healthcare applications > The new age of healthcare
What the sources say
- Partly supportedcites [3] Combi, 2016, Telemedicine for developing countries, Appl Clin Inform sourceExperimental — accuracy not yet measured
The Introduction says developing countries can benefit from telemedicine, and the Scalability section describes systems that can grow to serve more people and healthcare centers. The paper does not establish that developing countries’ healthcare systems are expanding or that their healthcare infrastructure can be designed to meet current needs; its Infrastructure section instead emphasizes existing barriers. (LLM, conf=0.86)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Deep Mind is also being looked at for applications including mobile medical assistant, diagnostics based on medical imaging, and prediction of patient deterioration [6], [7].”
§ The rise of artificial intelligence in healthcare applications > The new age of healthcare > Technological advancements
What the sources say
- Not matchedcites [6] King D, DeepMind’s health team joins Google Health.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Could not verifycites [7] Hoyt, 2016, IBM Watson Analytics: automating visualization, descriptive, and predictive statistics, JMIR Public Health Surveill sourceExperimental — accuracy not yet measured
The Introduction discusses IBM Watson Analytics, and Methods > Data describes datasets used to evaluate it. Searches for Deep Mind, a mobile medical assistant, imaging diagnostics, and prediction of patient deterioration found no passage addressing those applications. No page numbers are available in our copy. (LLM, conf=0.76)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“For instance, Wallach and Dzamba build AtomNet, a deep CNN to predict the bioactivity of small molecule drugs for drug discovery applications. The authors showed that AtomNet outperforms conventional docking models in relation to accuracy with an AUC (area under the curve) of 0.9 or more for 58% of the targets [25].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [25] Wallach, 2015, AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery, arXiv source
The Results section reports that AtomNet achieved an AUC of at least 0.9 on 59 of the full DUDE benchmark’s targets (57.8%), versus one target for the docking method Smina. The Conclusion describes AtomNet as a deep convolutional network for small-molecule bioactivity prediction and says it surpassed previous docking methods. (LLM, conf=0.94)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Omics-based tests: Genetic information from a population pool is used with machine learning algorithms to find correlations and predict treatment responses for the individual patient.”
§ The rise of artificial intelligence in healthcare applications > Precision medicine
What the sources say
- Partly supportedcites [12] Love-Koh, 2018, The future of precision medicine: potential impacts for health technology assessment, Pharmacoeconomics sourceExperimental — accuracy not yet measured
In Findings > Technological Developments (page not provided), the ‘Omics’-Based Biomarkers section says genetic and genomic tools can predict treatment response. The separate Complex Algorithms section says AI algorithms use large datasets that include genetic information to predict treatment choices for individual patients. The paper does not say that omics-based tests generally use machine-learning algorithms to find correlations in pooled population data; the claim combines two technology categories. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“After learning a new capability from the existing data, this can be applied to new images and the system can classify the images in the right category (Inference), similar to how a radiologist operates [28].”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Deep learning and medical image recognition
What the sources say
- Partly supportedcites [28] Lundervold, 2019, An overview of deep learning in medical imaging focusing on MRI, Z Med Phys sourceExperimental — accuracy not yet measured
Section 2 (p. 3) says supervised models learn from labeled data to assign labels to previously unseen data, including medical images. Sections 2.2–2.3 (p. 6) describe CNNs learning image features and producing outputs. The paper does not establish the added comparison that this operates like a radiologist. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Any failure during this process has a large financial impact, and in fact most drug candidates fail sometime during development and never make it onto the market.”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [14] Hughes, 2011, Principles of early drug discovery, Br J Pharmacol source
The Lead optimization phase says that only one in 10 candidates entering the clinical phase reaches the market, directly supporting the claim that most do not. It also says failure at that stage has much higher financial consequences. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“NLP is being used to analyze data from EMRs and gather large-scale information on the late-stage complications of a certain medical condition [26].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Natural language processing
What the sources say
- Partly supportedcites [26] Hashimoto, 2018, Artificial intelligence in surgery: promises and perils, Ann Surg sourceExperimental — accuracy not yet measured
The Natural Language Processing section (page not provided) says NLP has been used for large-scale analysis of EMR data to detect adverse events and postoperative complications. It does not establish the more specific claim about late-stage complications of a particular medical condition. (LLM, conf=0.85)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Drug discovery and development is an immensely long, costly, and complex process that can often take more than 10 years from identification of molecular targets until a drug product is approved and marketed.”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [14] Hughes, 2011, Principles of early drug discovery, Br J Pharmacol source
The Abstract (page not provided) describes developing a drug from the original idea to a launched product as a complex process that can take 12–15 years and cost over $1 billion. The Introduction traces the process from target selection through clinical development to a marketed medicine. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Blaschke et al. also applied adversarial autoencoders and Bayesian optimization to generate ligands specific to the dopamine type 2 receptor [22].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [22] Blaschke, 2018, Application of generative autoencoder in de novo molecular design, Mol Inform source
The Methods describe Bayesian optimization of the autoencoder’s latent space using predicted target activity as the objective, with DRD2 chosen as the target. The Conclusion states that the study used generative adversarial autoencoders and identified novel structures predicted to be active against DRD2. The paper describes predicted activity, not experimentally established receptor specificity. (LLM, conf=0.91)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“As an example, a video analysis of a laparoscopic procedure in real time has resulted in 92.8% accuracy in identification of all the steps of the procedure and surprisingly, the detection of missing or unexpected steps [26].”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Machine vision for diagnosis and surgery
What the sources say
- Supportedcites [26] Hashimoto, 2018, Artificial intelligence in surgery: promises and perils, Ann Surg source
The Computer Vision section reports that real-time analysis of laparoscopic video achieved 92.8% accuracy in identifying the steps of a sleeve gastrectomy and noted missing or unexpected steps. This supports the citing sentence’s example. (LLM, conf=0.93)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In addition to genetic information, other biomarkers such as protein expression, gut microbiome, and metabolic profile are also employed with machine learning to enable personalized treatments [12].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine
What the sources say
- Partly supportedcites [12] Love-Koh, 2018, The future of precision medicine: potential impacts for health technology assessment, Pharmacoeconomics sourceExperimental — accuracy not yet measured
In Findings > Technological Developments (page unavailable), the paper discusses proteomic and metabolomic biomarkers that may inform treatment choices, and AI-based algorithms that may recommend treatments using patient-level data. It does not identify gut microbiome biomarkers or say that the listed biomarkers are being used together with machine learning; it largely describes these developments as anticipated rather than established practice. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“An assistive robot named RIBA with human-type arms was designed to help patients with lifting and moving heavy things.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Assistive robots
What the sources say
- Partly supportedcites [57] Joseph A, Christian B, Abiodun AA, Oyawale F. A review on humanoid robotics in healthcare. In: MATEC Web of Conferences; 2018. https://www.matec-conferences.org/ sourceExperimental — accuracy not yet measured
Section 4.3, “Humanoid Robot for Aging Population,” says RIBA had human-type arms and was designed for heavy physical tasks, specifically transferring a person between a bed and wheelchair. The “Author Robot” table likewise describes it as lifting and transferring patients. These passages do not support the claim that it helped patients lift or move heavy things. (LLM, conf=0.88)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This machine learning-based activity recognition framework detected abnormality together with contextual factors such as object, space, time, and duration for decision support on suitable action to keep the patient safe in the given environment.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Smart home
What the sources say
- Supportedcites [55] Gayathri KS, Easwarakumar KS. Intelligent decision support system for dementia care through smart home. Procedia Comp Sci 2016;93:947–55. source
The Introduction says the MLN-based activity recognition framework detects abnormality together with object, time, space, and duration as contextual factors. Section 3 says the decision support system selects a suitable action for detected abnormality to enhance the occupant’s safety in the environment. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Uncertainty modeling is important for monitoring patients with dementia because activities conducted by the patient are typically incomplete in nature.
- Rests on an assumption: Domain knowledge related to the patient’s lifestyle combined with their medical history can enhance the probability of activity recognition and facilitate decision-making.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The recent advances in brain machine interfaces (BMIs) have shown that a system can be employed where the subjects’ intended and voluntary goal-directed wishes (electroencephalogram, EEG) can be stored and learned when a user “trains” an intelligent controller (an AI).”
§ The rise of artificial intelligence in healthcare applications > Robotics and artificial intelligence-powered devices > Neuroprosthetics
What the sources say
- Partly supportedcites [48] Iturrate, 2015, Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control, Sci Rep sourceExperimental — accuracy not yet measured
The Results describe a controller that learns reaching tasks using error-related EEG signals decoded while users monitor its actions. The Discussion says learned control policies can be stored, but it does not say the system stores users’ wishes or directly learns their intended goals from EEG; it says a fuller extension to goal-directed control remains to be demonstrated. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In one example, Markov Logic Network was used for activity recognition design to model both simple and composite activities and decide on appropriate alerts to process patient abnormality.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Smart home
What the sources say
- Partly supportedcites [55] Gayathri KS, Easwarakumar KS. Intelligent decision support system for dementia care through smart home. Procedia Comp Sci 2016;93:947–55. sourceExperimental — accuracy not yet measured
Section 3 describes an MLN-based system that recognizes activities, detects abnormalities, and chooses an action; Section 3.4 says it selects an appropriate alert. The passages do not say that the system models both simple and composite activities, so that detail is unsupported by this paper's text. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Because of this “reinforcement learning,” the system can potentially store single to several control “policies,” which allow for patient personalization [48].”
§ The rise of artificial intelligence in healthcare applications > Robotics and artificial intelligence-powered devices > Neuroprosthetics
What the sources say
- Supportedcites [48] Iturrate, 2015, Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control, Sci Rep source
The Discussion states that several control policies can be learned and stored as a repertoire, and that a target could have multiple ways of being reached depending on the user’s preferences. It also says the learned control policy depends on each user’s assessment, supporting personalization. (LLM, conf=0.91)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It has been demonstrated that the robot is able to carry the patient from the bed to a wheelchair and vice versa. Instructions can be provided to RIBA either by using tactile sensors using a method known as tactile guidance to teach by showing [57].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Assistive robots
What the sources say
- Supportedcites [57] Joseph A, Christian B, Abiodun AA, Oyawale F. A review on humanoid robotics in healthcare. In: MATEC Web of Conferences; 2018. https://www.matec-conferences.org/ source
Section 4.3 states that RIBA transferred a human from a bed to a wheelchair and back. It also describes instructions given through tactile sensors, as in the adjoining citing sentence. (LLM, conf=0.94)
boosteddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The Markov Logic Network used handles both uncertainty modeling and domain knowledge modeling within a single framework, thus modeling the factors that influence patient abnormality [55].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Smart home
What the sources say
- Supportedcites [55] Gayathri KS, Easwarakumar KS. Intelligent decision support system for dementia care through smart home. Procedia Comp Sci 2016;93:947–55. source
The Introduction states that MLN integrates probabilistic methods and knowledge engineering within a single framework. Section 3 describes the MLN-based activity recognition system as detecting factors in occupant behavior that contribute to abnormality, including location, time, duration, and objects. (LLM, conf=0.93)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This is combined with other efforts to employ DL to find molecules that can interact with the main proteases (Mpro or 3CLpro) of the virus, resulting in the disruption of the replication machinery of the virus inside the host [67], [68].”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Partly supportedcites [67] Zhang, 2020, Deep learning based drug screening for novel coronavirus 2019-nCov, Preprints sourceExperimental — accuracy not yet measured
The Introduction and Methods describe deep-learning screening for molecules predicted to interact with 2019-nCoV 3C-like protease, which the paper links to replicase processing. The Conclusion calls these potential protease–ligand pairs and says their binding needs further checking. The paper does not establish that the candidates disrupt viral replication inside host cells. (LLM, conf=0.86)
- Partly supportedcites [68] Zhang, 2020, Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors, Science sourceExperimental — accuracy not yet measured
The paper’s main text (indexed under Abstract; page unavailable) identifies Mpro as 3CLpro and reports that an α-ketoamide inhibitor suppresses SARS-CoV-2 replication in infected human cells. It describes crystal-structure-guided docking and inhibitor development, but does not report using deep learning to find molecules; that central part of the attributed claim is unsupported. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“A good example of this collaboration is seen in a recent breakthrough for antibiotic discovery, where the researchers devised/trained a neural network that actively “learned” the properties of a vast number of molecules in order to identify those that inhibit the growth of E. coli, a Gram negative bacterial species that is notoriously hard to kill [66].”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Partly supportedcites [66] Stokes, 2020, A deep learning approach to antibiotic discovery, Cell sourceExperimental — accuracy not yet measured
The Introduction and Results say the neural network was trained on 2,335 molecules to predict E. coli growth inhibition, then applied to libraries comprising more than 107 million molecules. The claim captures its purpose but blurs the distinction between the molecules used for training and the much larger number screened. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
In the paper
“There is already a large amount of evidence that AI algorithms are performing on par or better than humans in various tasks, for instance, in analyzing medical images or correlating symptoms and biomarkers from electronic medical records (EMRs) with the characterization and prognosis of the disease [1].”
§ The rise of artificial intelligence in healthcare applications > The new age of healthcare
What the sources say
- Not checked — no full text of the sourcecites [1] Miller, 2018, Artificial intelligence in medical practice: the question to the answer?, Am J Med source
boosted- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A perfect combination of increased computer processing speed, larger data collection data libraries, and a large AI talent pool has enabled rapid development of AI tools and technology, also within healthcare [5].”
§ The rise of artificial intelligence in healthcare applications > The new age of healthcare > Technological advancements
What the sources say
- Not matchedcites [5] Lee, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“In a recent study [30], two groups of surgical trainees were subjected to different methods for Mastoidectomy, where one group (n=18) would go through the standard training path and the other would train on a freeware VR simulator [the visible ear simulator (VES)]. At the end of the training, a significant improvement in surgical dissection was observed for those who trained with VR.”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Augmented reality and virtual reality in the healthcare space
What the sources say
- Not checked — no full text of the sourcecites [30] Frendø, 2020, Decentralized virtual reality training of mastoidectomy improves cadaver dissection performance: a prospective, controlled cohort study, Otol Neurotol source
plaindirectn = 18- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
In the paper
“The current clinical system would need a redesign to be able to use such genomics data and the benefits hereof [13].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Genetics-based solutions
What the sources say
- Not matchedcites [13] Kulski JK. Next-generation sequencing—an overview of the history, tools, and ‘omic’ applications; 2020.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All these datasets can be used to build predictive models that can help clinicians with diagnostics and various treatment decision support.”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“There are various tasks in the drug discovery process where machine learning can be used to streamline the tasks. This includes drug compound property and activity prediction, de novo design of drug compounds, drug–receptor interactions, and drug reaction prediction [16].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Not checked — no full text of the sourcecites [16] Zhang, 2017, From machine learning to deep learning: progress in machine intelligence for rational drug discovery, Drug Discov Today source
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A recent study, looking at the effect of immersive VR for patients who had suffered from chronic stroke patients, found this technology to be contributing positively to the state of patients.”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Augmented reality and virtual reality in the healthcare space
What the sources say
- Not checked — no full text of the sourcecites [31] Lee, 2020, Upper extremity rehabilitation using fully immersive virtual reality games with a head mount display: a feasibility study, PM R source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Here, by wearing a smart device or manual data entry for a prolonged period, individuals will be able to communicate to their healthcare workers without the need of disrupting their daily lives [35].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Health monitoring and wearables
What the sources say
- Not checked — no full text of the sourcecites [35] Athilingam, 2016, Features and usability assessment of a patient-centered mobile application (HeartMapp) for self-management of heart failure, Appl Nurs Res source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“An example where artificial tactile sensing has been used includes screening of breast cancer, as a replacement for clinical breast examination to complement medical imaging techniques such as x-ray mammography and MRI.”
§ The rise of artificial intelligence in healthcare applications > Robotics and artificial intelligence-powered devices > Minimally invasive surgery
What the sources say
- Not checked — no full text of the sourcecites [44] Naidu, 2017, A breakthrough in tumor localization: combining tactile sensing and ultrasound to improve tumor localization in robotics-assisted minimally invasive surgery, IEEE Robot Autom Mag source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“There is an ongoing transformation within surgical technology and focus has especially been placed in reducing the invasiveness of surgical procedure by minimizing incisions, reducing open surgeries, and using flexible tools and cameras to assist the surgery [42].”
§ The rise of artificial intelligence in healthcare applications > Robotics and artificial intelligence-powered devices > Minimally invasive surgery
What the sources say
- Not checked — no full text of the sourcecites [42] Konstantinova, 2014, Implementation of tactile sensing for palpation in robot-assisted minimally invasive surgery: a review, IEEE Sens J source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“DeepCare is an example of an AI-based platform for end-to-end processing of EMR data. It uses a deep dynamic memory neural network to read and store experiences and in memory cells.”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Not checked — no full text of the sourcecites [41] Pham T, Tran T, Phung D, Venkatesh S. DeepCare: a deep dynamic memory model for predictive medicine. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer; 2016. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Artificial tactile sensing has also been used for other applications including assessment of liver, brain, and submucosal tumors [45].”
§ The rise of artificial intelligence in healthcare applications > Robotics and artificial intelligence-powered devices > Minimally invasive surgery
What the sources say
- Not checked — no full text of the sourcecites [45] Madani, 2017, Quantitative diagnosis of breast tumors by characterization of viscoelastic behavior of healthy breast tissue, J Mech Behav Biomed Mater source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Assistive technologies are experiencing rapid growth, especially among people aged 65–74 years [51].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living
What the sources say
- Not checked — no full text of the sourcecites [51] Anderson, 2015, The impact of assistive technologies on formal and informal home care, Gerontologist source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Many of these apps use AI-powered tools to process the data collected from the robots in order to perform tasks such as facial recognition, object identification, language processing, and various diagnostic support [59].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Assistive robots
What the sources say
- Not checked — no full text of the sourcecites [59] Koumakis, 2019, Dementia care frameworks and assistive technologies for their implementation: a review, IEEE Rev Biomed Eng source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The long short-term memory of the system models the illness trajectory and healthcare processes of users via a time-stamped sequence of events and in this way allows capturing long-term dependencies [41].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Not checked — no full text of the sourcecites [41] Pham T, Tran T, Phung D, Venkatesh S. DeepCare: a deep dynamic memory model for predictive medicine. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer; 2016. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Using neural networks, an MIT-based lab has conducted research onto the detection of early signs of depression using speech.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Not matchedcites [63] Matheson R. Model can more naturally detect depression in conversations. MIT News; 2018.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“EMR providers such as Cerner, Epic, and Athena are beginning to add AI functionality such as NLP in their systems making it easier to access and extract data held in their libraries [39].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Not matchedcites [39] Davenport TH, Hongsermeier TM, Mc Cord KA. Using AI to improve electronic health records. Harvard Business Review; 2018.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“Recently, machine learning algorithms have been developed to detect words and intonations of an individual’s speech that may indicate a mood disorder.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Not matchedcites [63] Matheson R. Model can more naturally detect depression in conversations. MIT News; 2018.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the rapid advances in AI for imaging analysis, it seems likely that most radiology and pathology images will be examined at some point by a machine.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Furthermore, the development of models that can help clinicians detect postoperative complications such as infections will contribute toward a more efficient system [65].”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Not checked — no full text of the sourcecites [65] Hu, 2016, Accelerating chart review using automated methods on electronic health record data for postoperative complications, AMIA Annu Symp Proc
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 37
- Probably not a substantial claim. 35
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, mining of the large-scale chemistry data is needed to efficiently classify potential drug compounds and machine learning techniques have shown great potential [15].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [15] Ekins, 2019, Exploiting machine learning for end-to-end drug discovery and development, Nat Mater source
In “The tipping point for machine learning,” the paper describes large chemical and biological datasets as valuable for drug-discovery machine learning and says they must be prepared for modeling. “Machine learning models in action” describes using these methods to virtually screen and select compounds, supporting the citing sentence’s broad paraphrase. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“An example of this is a group at the Institute of Information Technology at the Alpen-Adria Universität Klagenfurt that uses surgery videos as training material in order to identify a specific intervention made by the surgeon. For example, when an act of dissection or cutting is performed on the patient’s tissues or organs, the algorithm recognizes the likelihood of the intervention as well as the”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Machine vision for diagnosis and surgery
What the sources say
- Supportedcites [27] Petscharnig, 2018, Learning laparoscopic video shot classification for gynecological surgery, Multimed Tools Appl source
The Introduction describes training classifiers on annotated gynecological surgery videos to predict surgical-action and anatomical-structure classes. The Action section includes blunt dissection and cutting among the actions classified. The paper supports identifying an anatomical structure in a video scene, rather than pinpointing its location within the frame. (LLM, conf=0.90)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“These elements are used to motivate and drive users to reach their goals [34].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Health monitoring and wearables
What the sources say
- Supportedcites [34] Johnson, 2016, Gamification for health and wellbeing: a systematic review of the literature, Internet Interv source
The Introduction (‘Gamification: a new model?’) describes game-design elements as motivational features, including goal-setting and progress feedback. It also says gamification aims to drive behaviour through the motivating qualities of games, supporting the sentence as a statement of purpose. (LLM, conf=0.91)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Current AI applications within healthcare are often standalone applications, these are often used for diagnostics using medical imaging and for disease prediction using remote patient monitoring [38].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Partly supportedcites [38] Rojahn, 2016, Remote monitoring of chronic diseases: a landscape assessment of policies in four European countries, PLoS One sourceExperimental — accuracy not yet measured
The Introduction (page not provided) says remote monitoring and trend analysis can detect deterioration in a patient’s condition early. It does not describe AI-based disease prediction, standalone AI applications, or diagnosis using medical imaging; its mention of transmitted still images is not evidence of AI imaging diagnostics. (LLM, conf=0.82)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Another platform called MoleculeNet is also used to translate two-dimensional molecular structures into novel features/descriptors, which can then be used in predicting toxicity of the given molecule. The MoleculeNet platform is built on data from various public databases and more than 700,000 compounds have already been tested for toxicity or other properties [20].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [20] Wu, 2018, MoleculeNet: a benchmark for molecular machine learning, Chem Sci source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Merk et al. trained a recurrent neural network to capture a large number of bioactive compounds such as SMILES strings. This model was then fine-tuned to recognize retinoid X and peroxisome proliferator-activated receptor agonists. The identified compounds were synthesized and demonstrated potent receptor modulatory activity in in vitro assays [23].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [23] Merk, 2018, De novo design of bioactive small molecules by artificial intelligence, Mol Inform source
The paper’s Abstract states that the authors trained an RNN on bioactive compounds represented as SMILES strings and fine-tuned it for RXR and PPAR agonists. It reports that five model-designed compounds were synthesized and four showed nanomolar to low-micromolar receptor-modulatory activity in cell-based assays; the claim should be read with that four-of-five qualification. (LLM, conf=0.91)
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Machine learning has also been implemented to assess the toxicity of molecules, for instance, using DeepTox, a DL-based model for evaluating the toxic effects of compounds based on a dataset containing many drug molecules [19].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Supportedcites [19] Mayr, 2016, DeepTox: toxicity prediction using deep learning, Front Environ Sci source
The Abstract describes DeepTox as a deep-learning pipeline for toxicity prediction and says the Tox21 challenge comprised 12,000 environmental chemicals and drugs tested for toxic effects. Section 2.3 describes how the pipeline uses compound data to generate predictions. (LLM, conf=0.91)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Studying data from a cohort of diabetic and mental health patients it was demonstrated that DeepCare could predict the progression of disease, optimal interventions, and assessing the likelihood for readmission [37].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Partly supportedcites [37] Pham, 2017, Predicting healthcare trajectories from medical records: a deep learning approach, J Biomed Inform sourceExperimental — accuracy not yet measured
The Conclusion reports that DeepCare was demonstrated on predicting next disease stages, recommending interventions, and estimating unplanned readmission among diabetic and mental health patients. But §4.3 evaluates prediction of interventions recorded for current diagnoses; it does not establish that the recommended interventions were optimal. (LLM, conf=0.89)
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“One main pain point refers to the lack of empathy and the ethical dilemma that can occur between AI and patients [70].”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > The digital primary physician
What the sources say
- Supportedcites [70] Blease, 2019, Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views, J Med Internet Res source
In Results, “Empathy and Communication” identifies inability to provide empathic care as a main perceived limitation of future technology. Results, “Social and Ethical Concerns,” also records concerns about safety, responsibility, and liability; the Discussion identifies risks including bias and patient privacy. These are concerns reported by GPs, not demonstrated harms in AI–patient interactions. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“In a recent study, when asked about the future of AI on primary care, while acknowledging its potential benefits, most practitioners were extremely skeptical regarding it playing a significant role in the future of the profession.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > The digital primary physician
What the sources say
- Supportedcites [70] Blease, 2019, Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views, J Med Internet Res source
The Discussion’s Principal Findings say that skepticism was the dominant view among respondents and that most GPs expected technological innovation to have a restricted scope in general practice. The Results’ Benefits of Future Technology in Primary Care section also reports perceived benefits, chiefly improved efficiency and reduced administrative work. (LLM, conf=0.92)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
In the paper
“The AI-associated healthcare market is expected to grow rapidly and reach USD 6.6 billion by 2021 corresponding to a 40% compound annual growth rate [4].”
§ The rise of artificial intelligence in healthcare applications > The new age of healthcare
What the sources say
- Not checked yetcites [4] Bresnick J. Artificial intelligence in healthcare market to see 40% CAGR surge; 2017.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Healthcare institutions are also fighting to keep up with all the new technological developments and the high expectations of patients with respect to levels of service and outcomes as they know it from consumer products including those of Amazon and Apple [2].”
§ The rise of artificial intelligence in healthcare applications > The new age of healthcare
What the sources say
- Not checked — no full text of the sourcecites [2] Kirch, 2017, Addressing the physician shortage: the peril of ignoring demography, JAMA source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For these patients, this immersive experience could act as a personal rehabilitation physiotherapist who engages their upper limb movement multiple times a day, allowing for possible neuroplasticity and a gradual return of normal motor function to these regions.”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Augmented reality and virtual reality in the healthcare space
What the sources say
- Not checked — no full text of the sourcecites [31] Lee, 2020, Upper extremity rehabilitation using fully immersive virtual reality games with a head mount display: a feasibility study, PM R source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“There are many areas in healthcare in which NLP can provide substantial benefits. Some of the more immediate applications include [36]”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Natural language processing
What the sources say
- Not checked yetcites [36] Rangasamy ASS, Nadenichek R, Rayasam M. Natural language processing in healthcare; 2018.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As an example, the program druGAN used adversarial autoencoders to generate new molecular fingerprints and drug designs incorporating features such as solubility and absorption based on predefined anticancer drug properties. These results suggest a substantial improvement in the efficiency in generating new drug designs with specific properties [21].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Not checked — no full text of the sourcecites [21] Kadurin, 2017, druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico, Mol Pharm source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Advantages of precision medicine include reduced healthcare costs, reduction in adverse drug response, and enhancing effectivity of drug action [11].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine
What the sources say
- Not checked — no full text of the sourcecites [11] Konieczny L, Roterman I. Personalized precision medicine. Bio-Algorithms Med-Syst 2019; 15. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In this way, molecular properties including octanol, solubility melting point, and biological activity can be evaluated as demonstrated by Coley et al. and others and be used to predict new features of the drug molecules [18].”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Not checked — no full text of the sourcecites [18] Coley, 2017, Convolutional embedding of attributed molecular graphs for physical property prediction, J Chem Inf Model source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“So game-like technologies such as VR and AR could enhance and enrich the learning experience for future medical and health-related disciplines [29].”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Augmented reality and virtual reality in the healthcare space
What the sources say
- Not checked yetcites [29] Chien CH, Chen CH, Jeng TS. An interactive augmented reality system for learning anatomy structure. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010; 2010. http://www.iaeng.org/publication/IMECS2018/
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The current educational system is limited, and this limitation is a hindrance for interactive disciplines such as medicine.
In the paper
“A study has shown that late-stage adult cancer patients can use this technology with minimum physical discomfort and in return benefit from an enhanced relaxed state, entertainment, and a much-needed distraction [32].”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Augmented reality and virtual reality in the healthcare space
What the sources say
- Not checked yetcites [32] Baños, 2013, A positive psychological intervention using virtual reality for patients with advanced cancer in a hospital setting: a pilot study to assess feasibility, Support Care Cancer
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The drug molecules and the associated features used in the in silico models are transformed into vector format so they can be read by the learning systems. Generally, the data used here include molecular descriptors (e.g., physicochemical properties) and molecular fingerprints (molecular structure) as well as simplified molecular input line entry system (SMILES) strings and grids for convolutional”
§ The rise of artificial intelligence in healthcare applications > Precision medicine > Drug discovery and development
What the sources say
- Not checked — no full text of the sourcecites [17] Lavecchia, 2019, Deep learning in drug discovery: opportunities, challenges and future prospects, Drug Discov Today source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Remote monitoring and picking up on early signs of disease could be immensely beneficial for those who suffer from chronic conditions and the elderly.”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Health monitoring and wearables
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For real-life and precise execution, AR would be more advantageous in healthcare settings.”
§ The rise of artificial intelligence in healthcare applications > Artificial intelligence and medical visualization > Augmented reality and virtual reality in the healthcare space
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“According to the researchers, the “model sees sequences of words/speaking style” and decides whether these emerging patterns are likely to be seen in individuals with and without depression [63].”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Not checked yetcites [63] Matheson R. Model can more naturally detect depression in conversations. MIT News; 2018.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“A review by Becker [62] suggests that AI used in healthcare can serve clinicians, patients, and other healthcare workers in four different ways. Here, we will use these suggestions as inspirations and will expand on their contribution toward a successful implementation of AI in healthcare: (Fig. 2.9 )1.Assessment of disease onset and treatment success.2.Management or alleviation of complications.3”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Not checked — no full text of the sourcecites [62] Becker, 2019, Artificial intelligence in medicine: what is it doing for us today?, Health Policy Technol source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Machine learning algorithms used in smart home applications include probabilistic and discriminative methods such as Naive Bayes classifier and Hidden Markov Model, support vector machine, and artificial neural networks [54].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Smart home
What the sources say
- Not checked — no full text of the sourcecites [54] Fahad LG, Ali A, Rajarajan M. Learning models for activity recognition in smart homes. In: Information science and applications. Berlin: Springer; 2015. p. 819–26. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The tactile sensory system can detect mass calcifications inside the breast tissue based on palpation of different points of the tissue and comparing with different reference data, and subsequently determine whether there are any significant abnormalities in the breast tissue.”
§ The rise of artificial intelligence in healthcare applications > Robotics and artificial intelligence-powered devices > Minimally invasive surgery
What the sources say
- Not checked — no full text of the sourcecites [44] Naidu, 2017, A breakthrough in tumor localization: combining tactile sensing and ultrasound to improve tumor localization in robotics-assisted minimally invasive surgery, IEEE Robot Autom Mag source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The Kompaï robot used for the MARIO project was developed by Robosoft and is a robot containing a camera, a Kinect motion sensor, and two LiDAR remote sensing systems for navigation and object identification [58].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Assistive robots
What the sources say
- Not checked — no full text of the sourcecites [58] D’Onofrio, 2019, MARIO Project: validation and evidence of service robots for older people with dementia, J Alzheimers Dis source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Assistive technologies increase the self-dependencies of patients, encouraging user participation in Information and Communication Technology (ICT) tools to provide remote care services type assistance and provide information to the healthcare professionals.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living
What the sources say
- Not checked — no full text of the sourcecites [51] Anderson, 2015, The impact of assistive technologies on formal and informal home care, Gerontologist source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Altogether, studies investigating cognitive stimulation seem to demonstrate a decrease in the rate of cognitive decline and progression of dementia.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Social and emotional stimulation
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Cognitive stimulation has been demonstrated to decrease cognitive impairment and can be trained using assistive robots.
In the paper
“In a study published in the journal of trauma and acute care surgery, researchers think that by understanding the microbiological niches (biomarkers) of trauma patients, we could hold the key to future wound infections and therefore can allow healthcare workers to take the necessary arrangements to prevent the worst outcome [64].”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
What the sources say
- Not checked — no full text of the sourcecites [64] Dente, 2017, Towards precision medicine: accurate predictive modeling of infectious complications in combat casualties, J Trauma Acute Care Surg source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Virtrael is one of such cognitive stimulation platforms and serves to assess, stimulate, and train various cognitive skills that experience a decline in the patient. The Virtrael program is based on visual memory training and the project is carried out by three different key functionalities: configuration, communication, and games.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Cognitive assistants
What the sources say
- Not checked — no full text of the sourcecites [60] Garcia-Alonso J, Fonseca C, editors. Gerontechnology: First International Workshop. In: First international workshop on gerotechnology. Springer; 2018. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“There are various international efforts in gathering EMR data across countries including Observational Health Data Science and Informatics (OHDSI), who have consolidated 1.26 billion patient records from 17 different countries [40].”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Not checked — no full text of the sourcecites [40] Wang, 2019, Deep learning in medicine—promise, progress, and challenges, JAMA Intern Med source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AAL applications typically collect data through sensors and cameras and apply various artificially intelligent tools for developing an intelligent system [52].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living
What the sources say
- Not checked — no full text of the sourcecites [52] Barnay, 2016, Does home care for dependent elderly people improve their mental health?, J Health Econ source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Most people want to preserve their autonomy, even at an old age, and maintain control over their lives and decisions [50].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living
What the sources say
- Not checked yetcites [50] Roberts AW, Ogunwole SU, Blakeslee L, Rabe MA, The population 65 years and older in the United States: 2016; 2018.
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“The games are intended to train cognitive skills of the patient including memory, attention, and planning (Fig. 2.8 ) [60].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Cognitive assistants
What the sources say
- Not checked — no full text of the sourcecites [60] Garcia-Alonso J, Fonseca C, editors. Gerontechnology: First International Workshop. In: First international workshop on gerotechnology. Springer; 2018. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Using the stored data, the framework of DeepCare can model disease progression, support intervention recommendation, and provide disease prognosis based on EMR databases.”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
What the sources say
- Not checked — no full text of the sourcecites [41] Pham T, Tran T, Phung D, Venkatesh S. DeepCare: a deep dynamic memory model for predictive medicine. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer; 2016. source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By daily collection of patient data, activities of daily living are defined over time and abnormalities can be detected as a deviation from the routine.”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Smart home
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Low-cost sensors in an Internet of Things architecture can be a useful way of detecting abnormal behavior in the home.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, integrating such standalone applications with EMR data could provide even greater value by adding personal medical data and history as well as a large statistical reference library to make classifications and predictions more accurate and powerful.”
§ The rise of artificial intelligence in healthcare applications > Intelligent personal health records > Integration of personal records
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“The robotic pet PARO, a baby seal robot, is the most widely used robotic pet and carries various sensors to sense touch, sounds, and visual objects [61].”
§ The rise of artificial intelligence in healthcare applications > Ambient assisted living > Social and emotional stimulation
What the sources say
- Not checked — no full text of the sourcecites [61] Vitanza A, D’Onofrio G, Ricciardi F, Sancarlo D, Greco A, Giuliani F. Assistive robots for the elderly: innovative tools to gather health relevant data. In: Data science for healthcare: methodologies and applications. Springer; 2019. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Machine learning techniques can also contribute toward the prediction of serious complications such as neuropathy that could arise for those suffering from type 2 diabetes or early cardiovascular irregularities.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Patient-care assistance technologies can improve the workflow for clinicians and contribute toward patient’s autonomy and well-being.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is also very important to note that at the time of an epidemic, an outbreak, natural or manmade disaster, or simply when the patient is away from their usual dwelling, a technology that allows humans to remotely interact and solve problems will have to become a necessity.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > The digital primary physician
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: At the time of writing (Early 2020), the threat of a SARS-COV-2 epidemic looms over many countries and is expanding at an unprecedented rate.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Speech and text recognition are already employed for tasks like patient communication and capture of clinical notes, and their usage will increase.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Perhaps the only healthcare providers who will risk their careers over time may be those who refuse to work alongside AI.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For widespread adoption to take place, AI systems must be approved by regulators, integrated with EHR systems, standardized to a sufficient degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organizations, and updated over time in the field.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: The greatest challenge to AI in healthcare domains is ensuring adoption in daily clinical practice, not whether technologies will be capable enough.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These challenges will ultimately be overcome, but they will take much longer to do so than it will take for the technologies themselves to mature.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The greatest challenge to AI in healthcare domains is ensuring adoption in daily clinical practice, not whether technologies will be capable enough.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If each patient is treated as an independent system, then based on the variety of designated data available, a bespoke approach can be implemented.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Over time, human clinicians may move toward tasks and job designs that draw on uniquely human skills like empathy, persuasion, and big-picture integration.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This setting can be applied both at home and in a hospital setting to relieve work pressure from healthcare workers and improve service.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10 years.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Combine this with the ever-increasing breakthroughs in the field of smart healthcare materials [71] and AI, one could envisage patients managing most of their own conditions at home and when necessary get in touch with a relevant healthcare worker who will refer them to more specialized physicians who could tend to their needs.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > The digital primary physician
What the sources say
- Not checked — no full text of the sourcecites [71] Zang, 2015, Advances of flexible pressure sensors toward artificial intelligence and health care applications, Mater Horiz source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Humanity prefers streamlining and creative solutions that are effective and take less out of our daily lives.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, patients will be able to interact with the device in a remote manner and access their biometric data, all the while feeling that they are interacting with a caring and empathetic system that truly wants the best outcome for them.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although early efforts at providing diagnosis and treatment recommendations have proven challenging, we expect that AI will ultimately master that domain as well.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Artificial intelligence in the near and the remote
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI can accelerate the diagnosis process and medical research.”
§ The rise of artificial intelligence in healthcare applications > The artificial intelligence can see you now > Success factors for artificial intelligence in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Demand for healthcare services is ever increasing and many countries are experiencing a shortage of healthcare practitioners, especially physicians.
“The demand for healthcare services is ever increasing and many countries are experiencing a shortage of healthcare practitioners, especially physicians.”
The paper relies on this without support.
AI applications can cut annual US healthcare costs by USD 150 billion in 2026.
“It is estimated that AI applications can cut annual US healthcare costs by USD 150 billion in 2026.”
The paper relies on this without support.
A large part of these cost reductions stem from changing the healthcare model from a reactive to a proactive approach, focusing on health management rather than disease treatment.
“A large part of these cost reductions stem from changing the healthcare model from a reactive to a proactive approach, focusing on health management rather than disease treatment.”
The paper relies on this without support.
This change is expected to result in fewer hospitalizations, less doctor visits, and less treatments.
“This is expected to result in fewer hospitalizations, less doctor visits, and less treatments.”
The paper relies on this without support.
There is a significant shortage of experts in medical image interpretation.
“However, the demand is ever increasing and there is a significant shortage of experts in the field.”
The paper relies on this without support.
Many inherited diseases result in symptoms without a specific diagnosis and interpreting whole genome data is still challenging due to the many genetic profiles.
“Many inherited diseases result in symptoms without a specific diagnosis and while interpreting whole genome data is still challenging due to the many genetic profiles.”
The paper relies on this without support.
There has been a substantial increase in the amount of data available assessing drug compound activity and biomedical data in the past few years.
“There has been a substantial increase in the amount of data available assessing drug compound activity and biomedical data in the past few years.”
The paper relies on this without support.
The smart tissue autonomous robot (STAR) can outperform human surgeons in some surgical procedures such as bowel anastomosis in animals.
“The smart tissue autonomous robot (STAR) from the Johns Hopkins University has demonstrated that it can outperform human surgeons in some surgical procedures such as bowel anastomosis in animals.”
The paper relies on this without support.
Video data is estimated to contain 25 times the amount of data from high-resolution diagnostic images such as CT.
“Video data is estimated to contain 25 times the amount of data from high-resolution diagnostic images such as CT and could thus provide a higher data value based on resolution over time.”
The paper relies on this without support.
The current educational system is limited, and this limitation is a hindrance for interactive disciplines such as medicine.
“The current educational system is limited and for interactive disciplines such as medicine this can be a hindrance.”
The paper relies on this without support.
A patient-centric personal health record promotes self-management and improves outcomes for patients.
“However, in order to promote self-management and improve the outcomes for patients, a patient-centric personal health record should be implemented.”
The paper relies on this without support.
Wearable health devices allow for constant measurement of certain vital signs under various conditions.
“Wearable health devices (WHDs) are an upcoming technology that allow for constant measurement of certain vital signs under various conditions.”
The paper relies on this without support.
Minimal invasive surgery requires different motor skills compared with conventional surgery due to lower tactile feedback.
“Minimal invasive surgery requires different motor skills compared with conventional surgery due to the lower tactile feedback when relying more on tools and less on direct touching.”
The paper relies on this without support.
Artificial tactile sensing offers advantages over physical touching, including a larger reference library, standardization among surgeons, continuous improvement, and level of training.
“Artificial tactile sensing offers several advantages compared with physical touching including a larger reference library to compare sensation and standardization among surgeons with respect to quantitative features, continuous improvement, and level of training.”
The paper relies on this without support.
Smart homes can be useful for people with dementia.
“Smart homes can be useful for people with dementia and several studies have investigated smart home applications to facilitate the lives of dementia patients.”
The paper relies on this without support.
Low-cost sensors in an Internet of Things architecture can be a useful way of detecting abnormal behavior in the home.
“Low-cost sensors in an Internet of Things (IoT) architecture can be a useful way of detecting abnormal behavior in the home.”
The paper relies on this without support.
Most robots currently under development have some level of AI technology incorporated for better performance.
“Most of the robots currently under development have some level of AI technology incorporated for better performance with regard to classifications, language recognition, image processing, and more.”
The paper relies on this without support.
There are more than a thousand EMR providers operating in the United States alone.
“These use various operating systems and approaches with more than a thousand EMR providers operating in the United States alone.”
The paper relies on this without support.
The only data often missing is data from in between interventions and between hospital visits when the patient is well or may not be showing symptoms.
“The only data often missing is data from in between interventions and between hospital visits when the patient is well or may not be showing symptoms.”
The paper relies on this without support.
Uncertainty modeling is important for monitoring patients with dementia because activities conducted by the patient are typically incomplete in nature.
“Uncertainty modeling is important for monitoring patients with dementia as activities conducted by the patient are typically incomplete in nature.”
The paper relies on this without support.
Domain knowledge related to the patient’s lifestyle combined with their medical history can enhance the probability of activity recognition and facilitate decision-making.
“Domain knowledge related to the patient’s lifestyle is also important and combined with their medical history it can enhance the probability of activity recognition and facilitate decision-making.”
The paper relies on this without support.
Many elderly people experience a decline in their cognitive abilities and have difficulties in problem-solving tasks as well as maintaining attention and accessing their memory.
“Many elderly people experience a decline in their cognitive abilities and have difficulties in problem-solving tasks as well as maintaining attention and accessing their memory.”
The paper relies on this without support.
Cognitive stimulation is a common rehabilitation approach after brain injuries from stroke, multiple sclerosis or trauma, and various mild cognitive impairments.
“Cognitive stimulation is a common rehabilitation approach after brain injuries from stroke, multiple sclerosis or trauma, and various mild cognitive impairments.”
The paper relies on this without support.
Cognitive stimulation has been demonstrated to decrease cognitive impairment and can be trained using assistive robots.
“Cognitive stimulation has been demonstrated to decrease cognitive impairment and can be trained using assistive robots.”
The paper relies on this without support.
Companion robots assist elderly patients with their stress or depression by connecting emotionally with the patient with enhanced social interaction and assistance with various daily tasks.
“Such robots assist elderly patients with their stress or depression by connecting emotionally with the patient with enhanced social interaction and assistance with various daily tasks.”
The paper relies on this without support.
AI is increasingly becoming an integral part of all our lives, from smartphones to cars and healthcare.
“AI is increasingly becoming an integral part of all our lives. From smartphones to cars and more importantly our healthcare.”
The paper relies on this without support.
The greatest challenge to AI in healthcare domains is ensuring adoption in daily clinical practice, not whether technologies will be capable enough.
“The greatest challenge to AI in these healthcare domains is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice.”
The paper relies on this without support.
Prediction and assessment of a condition is something individuals will demand to have more control over in the coming years.
“Prediction and assessment of a condition is something that individuals will demand to have more control over in the coming years.”
The paper relies on this without support.
The general feeling of being unwell and its various complications that accompany mild illnesses are usually well tolerated by patients.
“The general feeling of being unwell and its various complications that accompany mild illnesses are usually well tolerated by patients.”
The paper relies on this without support.
The above scenario is the normal routine in most countries.
“The above scenario is the normal routine in most countries.”
The paper relies on this without support.
The current primary care system is not ideal and patients are likely to walk away dissatisfied.
“However, despite the helpfulness of the physician, it is not an ideal system and it is likely that if you were in the position of the above patient, you will walk away dissatisfied with the care received.”
The paper relies on this without support.
Frustration with such systems has led to an immense pressure on health workers and needs to be addressed.
“The frustration with such systems has led to an immense pressure on the health workers and needs to be addressed.”
The paper relies on this without support.
Trained AI bots can qualify whether certain symptoms warrant an actual conversation with a physician.
“Prior to having access to an actual doctor, trained AI bots can qualify whether certain symptoms warrant an actual conversation with a physician.”
The paper relies on this without support.
Questions and answers in AI triage are often vigorously reviewed by medical professionals at each stage to account for accuracy.
“These questions and answers are often vigorously reviewed by medical professionals at each stage to account for accuracy.”
The paper relies on this without support.
Remote digital visits allow patients to provide ample information for the physician to review before talking to the patient.
“Normally, the patient books an appointment for a specific time, often during the same day. This provides them with ample time to provide as much information as possible for the physician responsible to review and carefully analyze the evidence before talking to the patient.”
The paper relies on this without support.
Humanity prefers streamlining and creative solutions that are effective and take less out of our daily lives.
“Humanity prefers streamlining and creative solutions that are effective and take less out of our daily lives.”
The paper relies on this without support.
At the time of writing (Early 2020), the threat of a SARS-COV-2 epidemic looms over many countries and is expanding at an unprecedented rate.
“At the time of writing (Early 2020), the threat of a SARS-COV-2 epidemic looms over many countries and is expanding at an unprecedented rate.”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Achieving high predictive performance in healthcare often requires ML models with high flexibility to capture complex patterns in high-dimensional data [41].”
From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [41]
Experimental — accuracy not yet measuredIn the citing paper
“By combining these data with other relevant information, such as demographic and environmental factors, AI can be used to make accurate predictions about future health outcomes and develop targeted interventions to prevent disease outbreaks [3,4].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [3]
In the citing paper
“AI-driven healthcare platforms can utilize facial recognition and voice analysis to detect emotions in patients during interactions [129].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [129]
In the citing paper
“Moreover, while this manuscript focuses on the use of AI in transplantation, many other domains could benefit from it.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [61]
Citations
Cites
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Zhang, L., Lin, D., Sun, X., Curth, U., Drosten, C., Sauerhering, L., Becker, S., Rox, K., & Hilgenfeld, R. (2020) · Science · doi:10.1126/science.abb3405 · Cited by 2,895
Principles of early drug discovery
Hughes, J., Rees, S., Kalindjian, S., & Philpott, K. (2011) · British Journal of Pharmacology · doi:10.1111/j.1476-5381.2010.01127.x · Cited by 2,859
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463
An overview of deep learning in medical imaging focusing on MRI
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Artificial intelligence in surgery: promises and perils
Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468
Advances of flexible pressure sensors toward artificial intelligence and health care applications
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Gamification for health and wellbeing: A systematic review of the literature
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An Integrated Brain-Machine Interface Platform With Thousands of Channels
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Research works printed but not linked: 2
- Kalis B, Collier M, Fu R. 10 promising AI applications in health care. Harvard Business Review; 2018.
- Davenport TH, Hongsermeier TM, Mc Cord KA. Using AI to improve electronic health records. Harvard Business Review; 2018.
Cited by
Sorted by citations
Manickam, P., Mariappan, S. A., Murugesan, S. M., Hansda, S., Kaushik, A. K., Shinde, R. B., & Thipperudraswamy, S. P. (2022) · Biosensors · doi:10.3390/bios12080562 · Cited by 717
Enhancing mental health with Artificial Intelligence: Current trends and future prospects
Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F. T., & Eberhardt, J. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100099 · Cited by 667
When combinations of humans and AI are useful: A systematic review and meta-analysis
Vaccaro, M., Almaatouq, A., & Malone, T. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-02024-1 · Cited by 570
Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.04.001 · Cited by 468
Bekbolatova, M., Mayer, J., Ong, C. W., & Toma, M. (2024) · Healthcare · doi:10.3390/healthcare12020125 · Cited by 450
Metadata
- Authors
- Bohr, A., & Memarzadeh, K.
- Venue
- Artificial Intelligence in Healthcare
- Publisher
- Elsevier
- Published
- 2020
- Pages
- 25-60
- PMC
- PMC7325854
- OpenAlex
- W3038004684
- Semantic Scholar
- fd41c526ab65ef21a43dfd2b427f8eaeea6c35ce
- Cited by
- 1,636
- References
- 61
- Citations by year
- 2026: 180 · 2025: 400 · 2024: 557 · 2023: 327 · 2022: 114 · 2021: 56 · 2020: 1
- FWCI
- 73.16
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Elsevier eBooks (publishedVersion)
- PubMed Central (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- artificial intelligence in healthcare, machine learning, big data, clinical decision support, medical image analysis, medical device automation, patient monitoring, clinical documentation, administrative workflow, drug development, ambient assisted living, healthcare value chain
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.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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