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Cited by 235Internet of Things and AIArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective

Open access
khan, Z. Faizal, Alotaibi, Sultan Refa·2020·Journal of Healthcare Engineering
DOI 10.1155/2020/8894694PMC PMC7481991
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Abstract

Mobile health (m-health) is the term of monitoring the health using mobile phones and patient monitoring devices etc. It has been often deemed as the substantial breakthrough in technology in this modern era. Recently, artificial intelligence (AI) and big data analytics have been applied within the m-health for providing an effective healthcare system. Various types of data such as electronic health records (EHRs), medical images, and complicated text which are diversified, poorly interpreted, and extensively unorganized have been used in the modern medical research. This is an important reason for the cause of various unorganized and unstructured datasets due to emergence of mobile applications along with the healthcare systems. In this paper, a systematic review is carried out on application of AI and the big data analytics to improve the m-health system. Various AI-based algorithms and frameworks of big data with respect to the source of data, techniques used, and the area of application are also discussed. This paper explores the applications of AI and big data analytics for providing insights to the users and enabling them to plan, using the resources especially for the specific challenges in m-health, and proposes a model based on the AI and big data analytics for m-health. Findings of this paper will guide the development of techniques using the combination of AI and the big data as source for handling m-health data more effectively.

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review synthesizes applications of AI and big data analytics in m-health and proposes a combined AI/big-data model for real-time decision support. It reports 106 included articles and summarizes specific AI applications with accuracy/outcome figures, while noting access, accuracy, dependence, and privacy/security limitations.

Key findings

  • From 2,543 records retrieved from eight scientific databases plus 130 additional Google Scholar articles, 2,437 were excluded in initial screening; exclusions included 1,345 articles not representing AI in m-health, 902 not representing big data analytics in m-health, 78 from international journals, and 12 with insufficient details. A total of 106 articles were kept for review.
  • AI applications reviewed include: Larburu et al. reduced false heart-failure alerts from 28.64 to 7.8 per patient per year; Burns et al. reported 60%–91% accuracy for location prediction with eight patients; Hawley et al. reported 67% speech recognition accuracy with nine persons; Martin et al. reported 70% positive predictive value and a 50% reduction in unplanned hospital events with 214 patients over six months; Morrison et al. studied 76 participants over 72 hours; Ortiz-Catalan et al. reported about 50% reduction in phantom limb pain symptoms after six months with 14 participants.
  • Big data analytics in m-health is described through frameworks for healthcare data sources, transformation, big data platforms, analytics, patient-centered collaborative filtering, ubiquitous healthcare vital signs, health informatics processing pipelines, and smart healthcare layers. Data are classified as organized and unorganized, with examples including EHRs, EMRs, CT/MRI images, laboratory data, prescriptions, social media, and mobile data.
  • The proposed model has three essential parts: medical data obtained from patients through mobile phones and telemonitoring devices, an AI and big data analytics platform, and output toward a mobile care monitor. The AI-based engine includes a stream analysis module and an AI-based report management tool; the big data analysis engine includes big data storage and a statistical data analysis tool.

Methods

  • A systematic review followed the PRISMA checklist, searching IEEE Xplore, ACM Digital Library, Taylor & Francis Online, ScienceDirect, SAGE Journals, ProQuest, Springer, and Web of Science, plus Google Scholar. The review was limited to English articles and reports from 2007 to the present date.
  • Articles were screened using inclusion and exclusion criteria; duplicates were removed, and 106 articles were included for review. The paper also proposes a model based on AI and big data analytics for m-health.
  • The main analysis is a narrative synthesis of AI algorithms/frameworks and big data analytics applications in m-health and healthcare, with additional summary tables; no quantitative meta-analysis is reported.

Limitations

  • Stated in the paper: a large section of the population is denied access to m-health systems because of their numbers, inability to afford it, and lack of knowledge and skill to use it; the system can never be too accurate to replace humans; users may become completely dependent on the technology; and there are several privacy and security issues, including possible temporary or permanent loss of information and leakage to unauthorized users.
  • Stated in the paper: the review is limited to English articles and reports from 2007 to the present date.
  • Evident from the text: the proposed AI and big data m-health model is conceptual and is not clinically validated or evaluated in the paper; despite mention of PRISMA and meta-analyses, no quantitative meta-analysis is presented.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1155/2020/8894694

Cites

103 of 103 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 32 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    A total of 2543 articles were retrieved from the eight scientific databases.

    “A total of 2543 articles were retrieved from the eight scientific databases.”
    Quote in the paperNot a substantial claim
  • Premisecitation

    Various sensors such as camera sensor, microphone sensor, accelerometer sensor, and gyroscope sensor were used in healthcare-based applications.

    “Various sensors such as camera sensor [53–55], microphone sensor [56–58], accelerometer sensor [59–61], and gyroscope sensor [59, 62] were used in the healthcare-based applications.”
    Quote in the paperNot a substantial claim0 of 9 sources checked
    • cites [53] WHO Health: New Horizons for Health through Mobile Technologies 2011 Geneva, Switzerland World Health Organization

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [54] Bastawrous A. Increasing access to eye care there’s an app for that. Peek: smartphone technology for eye health International Journal of Epidemiology 2016 45 1040 1043 10.1093/ije/dyw086 2-s2.0-85018515816 27215615 source

      Not checked in the free run.

    • cites [55] Eysenbach G. Sawesi S. Jones J. Acceptability, usability, and views on deployment of peek, a mobile phone mhealth intervention for eye care in kenya: qualitative study JMIR mHealth and uHealth 2016 4 2 PMC4877502 27160779 source

      Not checked in the free run.

    • cites [56] Larson E. C. Goel M. Boriello G. Heltshe S. Rosenfeld M. Patel S. N. SpiroSmart: using a microphone to measure lung function on a mobile phone Proceedings of the 2012 ACM Conference on Ubiquitous Computing—UbiComp’12 September 2012 Pittsburgh, PA, USA 10.1145/2370216.2370261 2-s2.0-84867463319 source

      Not checked in the free run.

    • cites [57] Lin Y.-C. Lai Y.-H. Chang H.-W. SmartHear: a smartphone-based remote microphone hearing assistive system using wireless technologies IEEE Systems Journal 2018 12 1 20 29 10.1109/jsyst.2015.2490104 2-s2.0-85031717433 source

      Not checked in the free run.

    • cites [58] Thap T. Chung H. Jeong C. High-resolution time-frequency spectrum-based lung function test from a smartphone microphone Sensors 2016 16 8 10.3390/s16081305 2-s2.0-84983502282 PMC5017470 27548164 source

      Not checked in the free run.

    • cites [59] Catal C. Tufekci S. Pirmit E. Kocabag G. On the use of ensemble of classifiers for accelerometer-based activity recognition Applied Soft Computing 2015 37 1018 1022 10.1016/j.asoc.2015.01.025 2-s2.0-84947128497 source

      Not checked in the free run.

    • cites [60] Natale V. Drejak M. Erbacci A. Tonetti L. Fabbri M. Martoni M. Monitoring sleep with a smartphone accelerometer Sleep and Biological Rhythms 2012 10 4 287 292 10.1111/j.1479-8425.2012.00575.x 2-s2.0-84867140322 source

      Not checked in the free run.

    • cites [61] Thammasat E. Chaicharn J. A simply fall-detection algorithm using accelerometers on a smartphone Proceedings of the 2012 5th Biomedical Engineering International Conference (BMEiCON) December 2012 Ubon Ratchathani, Thailand 1 4 10.1109/bmeicon.2012.6465471 2-s2.0-84875097002 source

      Not checked in the free run.

  • Premisecitation

    m-Health can provide support in vulnerable and remote communities via improvements to networks and the emergence of IoT.

    “m-Health can provide support in vulnerable and remote communities via improvements to networks and the emergence of IoT [44].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [44] Madanian S. Airehrour D. Kumar N. Smart cap for visually impaired in disaster situations Proceedings of the ISCRAM Asia Pacific 2018 Wellington, New Zealand

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Premisecitation

    Larburu et al.'s predictive models were based on clinical data from 242 heart failure patients' mobile accumulated over 44 months.

    “These predictive models are based on clinical data taken from 242 heart failure patients' mobile accumulated in 44 months.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [96] Larburu N. Artetxe A. Escolar V. Lozano A. Kerexeta J. Artificial intelligence to prevent mobile heart failure patients decompensation in real time: monitoring-based predictive model Mobile Information Systems 2018 2018 11 1546210 10.1155/2018/1546210 2-s2.0-85057069022 source

      Not checked in the free run.

  • Premisecitation

    Larburu et al.'s proposed model lowered false alerts for a patient for a year from 28.64 to 7.8 gradually.

    “This proposed model lowered the false alerts for a patient for a year from 28.64 to 7.8 gradually.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [96] Larburu N. Artetxe A. Escolar V. Lozano A. Kerexeta J. Artificial intelligence to prevent mobile heart failure patients decompensation in real time: monitoring-based predictive model Mobile Information Systems 2018 2018 11 1546210 10.1155/2018/1546210 2-s2.0-85057069022 source

      Not checked in the free run.

  • Classified as probably not a substantial claim. 30
  • May combine two claims. 5

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    106 articles were obtained and kept for the review process.

    “At last, 106 articles were obtained and kept for the review process.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Among the excluded articles, 1345 did not represent applications of AI in m-health.

    “1345 articles which do not represent the applications of AI in m-health”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Among the excluded articles, 12 had insufficient details.

    “12 articles with insufficient details were excluded”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Among the excluded articles, 902 did not represent applications of big data analytics in m-health.

    “902 articles which do not represent the applications of big data analytics in m-health”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Another 130 additional articles were found through the search in Google Scholar.

    “Then, another 130 additional articles were found through the search in Google Scholar.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    A total of 2437 articles were excluded in the initial screening process.

    “A total of 2437 articles have been excluded in the initial screening process.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Among the excluded articles, 78 were from international journals.

    “78 articles which were from international journals”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Articles were excluded if not in English, lacking full text, not representing applications of AI in m-health, not representing applications of big data analytics in m-health, or with insufficient details.

    “The articles that were not in English, the articles lacking full text, the articles that do not represent the applications of AI in m-health, the articles that do not represent the applications of big data analytics in m-health, and the articles with insufficient details were excluded.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation

    The methodology of the review followed the PRISMA checklist.

    “The methodology of our review followed the checklist proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [35].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35] Moher D. Liberati A. Tetzlaff J. Altman D. G. Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement PLoS Medicine 2009 6 7 e1000097 PMC2707599 19621072 source

      Not checked in the free run.

    • Rests on an assumption: There is a deficiency of understanding the most suitable framework based on computational methodologies required for m-health.
    • Rests on an assumption: The main problems of big data analytics and m-health are yet to be solved.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The review was limited to English articles and reports from 2007 to present date.

    “The review is limited to English articles and reports from 2007 to present date.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The search terms used were "Artificial Intelligence AND m-Health," "Big data analytics AND m-Health," and "AI AND big data analytics in M-health."

    “The following terms were used in the search: “Artificial Intelligence AND m-Health,” “Big data analytics AND m-Health,” and “AI AND big data analytics in M-health.””
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    All duplicate articles were removed.

    “All the duplicate articles were removed.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The review searched eight large scientific databases: IEEE Xplore, ACM digital library, Taylor & Francis online, ScienceDirect, SAGE Journals, ProQuest, Springer, and Web of Science.

    “we searched eight large scientific databases: the IEEE Xplore, ACM digital library, Taylor & Francis online, ScienceDirect, SAGE Journals, ProQuest, Springer, and Web of Science.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Low-cost smartphones have the required features and capabilities to cope with health-related applications and include the necessary connectivity.

    “Low-cost smartphones have the required features and capabilities to cope with health-related applications and include the necessary connectivity [36].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Istepanian R. S. H. Woodard B. m-health: Fundamentals and Applications 2017 Hoboken, NJ, USA John Wiley

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Premisecitation

    AI has been applied in various domains of healthcare including cancer research, cardiology, diabetes, mental health, identification of prognosis, Alzheimer's disease, identification of difference in clinical groups, cardiovascular disease, and stroke-related studies.

    “AI has been put in application in various domains of healthcare [83–87] which includes cancer research [88], cardiology [89], diabetes [90], mental health [91], identification of prognosis [92], identification of Alzheimer's disease [93], identification of difference in the clinical groups [94], identification of cardiovascular disease [39], stroke-related studies [95], etc.”
    Quote in the paperNot a substantial claim0 of 7 sources checked
    • cites [83] Churpek M. M. Yuen T. C. Winslow C. Meltzer D. O. Kattan D. P. Edelson D. P. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards Critical Care Medicine 2016 44 2 368 374 10.1097/ccm.0000000000001571 2-s2.0-84954349720 26771782 PMC4736499 source

      Not checked in the free run.

    • cites [84] Seetharam K. Shrestha S. Sengupta P. P. Artificial intelligence in cardiovascular medicine Current Treatment Options in Cardiovascular Medicine 2019 21 6 10.1007/s11936-019-0728-1 2-s2.0-85065922543 PMC7561035 31089906 source

      Not checked in the free run.

    • cites [85] Bai W. Sinclair M. Tarroni G. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks Journal of Cardiovascular Magnetic Resonance 2018 20 65 10.1186/s12968-018-0471-x 2-s2.0-85053360253 PMC6138894 30217194 source

      Not checked in the free run.

    • cites [86] Al’Aref S. J. Anchouche K. Singh G. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging European Heart Journal 2019 40 24 1975 1986 10.1093/eurheartj/ehy404 2-s2.0-85068474187 30060039 source

      Not checked in the free run.

    • cites [87] Seetharam K. Kagiyama N. Sengupta P. P. Application of mobile health, telemedicine and artificial intelligence to echocardiography Echo Research and Practice 2019 6 2 41 52 10.1530/erp-18-0081 2-s2.0-85065758860 PMC6432977 30844756 source

      Not checked in the free run.

    • cites [88] Forsyth A. W. Barzilay R. Hughes K. S. Machine learning methods to extract documentation of breast cancer symptoms from electronic health records Journal of Pain and Symptom Management 2018 55 6 1492 1499 10.1016/j.jpainsymman.2018.02.016 2-s2.0-85044533302 29496537 source

      Not checked in the free run.

    • cites [89] Weng S. F. Reps J. Kai J. Garibaldi J. M. Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One 2017 12 4 e0174944 10.1371/journal.pone.0174944 2-s2.0-85016944795 PMC5380334 28376093 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Even the most well designed and technologically best developed apps can never be hundred percent accurate.

    “Even the most well designed and technologically best developed apps can also never be hundred percent accurate.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Fang et al. focused on a novel framework called "Health informatics processing pipeline" which incorporates steps to obtain significant patterns from healthcare-based big data.

    “They focused on a novel framework called “Health informatics processing pipeline” which incorporates various steps to obtain significant patterns from healthcare-based big data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Fang R. Pouyanfar S. Yang Y. Chen S.-C. Iyengar S. S. Computational health informatics in the big data age ACM Computing Surveys 2016 49 1 1 36 10.1145/2932707 2-s2.0-84975077478 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    These m-health systems make a user/patient dependent completely on them.

    “These m-health systems also make a user/patient to be dependent completely on them.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationreasoning

    The proposed model enhances the overall performance of m-health because AI and big data analytics are combined.

    “The proposed model enhances the overall performance of m-health since AI and big data analytics are combined.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    The proposed combination of AI and big data analytics enables a decision-making process in real time in m-health.

    “These are combined to convey the perception of enabling a decision-making process in real time.”
    • Also stated in: § 6. Proposed Model Based on AI and Big Data Analytics for m-Health · plain
      “The proposed methodology improves the process of m-health by processing each and every query, and it also enables a decision-making process in real time.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The proposed methodology improves the process of m-health by processing each and every query.

    “The proposed methodology improves the process of m-health by processing each and every query, and it also enables a decision-making process in real time.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Analytical approaches in big data can identify anomalies by analyzing huge amounts of data from various datasets and sources such as biomedical signals, physiological sensing data, genomic data, and biomedical imaging.

    “These analytical approaches in big data can be used to identify the anomalies by analyzing a huge amount of data from various datasets and their sources such as biomedical signals, physiological sensing data, genomic data, and biomedical imaging.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    These m-health systems cannot be a substitute to human.

    “These systems have been made to ease out the health structure but they cannot be a substitute to human.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    If the user loses his or her mobile phone and user id/password, there is a possibility for all the information to be lost temporarily or even permanently.

    “If the user loses his or her mobile phone and user id/password, there is a possibility for all the information to be lost temporarily or even permanently.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    There might be a chance for various issues in the privacy and security of the health data present in m-health systems.

    “There might be a chance for various issues in the privacy and security of the health data present in it.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Chawla and Davis proposed a patient-centric personalized framework for healthcare based on collaborative filtering approach.

    “A patient-centric personalized framework for healthcare based on the collaborative filtering approach was proposed by Chawla and Davis in [113].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [113] Chawla N. V. Davis D. A. Bringing big data to personalized healthcare: a patient-centered framework Journal of General Internal Medicine 2013 28 S3 660 665 10.1007/s11606-013-2455-8 2-s2.0-84892160333 23797912 PMC3744281

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Raghupathi and Raghupathi proposed a novel architecture for healthcare-based system applying big data analytics.

    “Raghupathi and Raghupathi [112] proposed a novel architecture for the healthcare-based system applying the analytics of big data.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [112] Raghupathi W. Raghupathi V. Big data analytics in healthcare: promise and potential Health Information Science and Systems 2014 2 3 10.1186/2047-2501-2-3 PMC4341817 25825667

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning

    In such cases, there is a chance for personal information to be leaked and shared to unauthorized users.

    “In such cases, there is a chance for the personal information to be leaked and shared to unauthorized users.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    The system can never be too accurate to replace humans and their predictions.

    “The system can never be too accurate to replace the humans and their predictions.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Kim et al. proposed an analytical framework of big data that employs ubiquitous healthcare system.

    “An analytical framework of big data that employs ubiquitous healthcare system was proposed by Kim et al. in [114].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [114] Kim T. W. Park K. H. Yi S. H. Kim H. C. A big data framework for u-healthcare systems utilizing vital signs Proceedings of the International Symposium on Computer, Consumer and Control June 2014 Taichung, Taiwan 494 497 10.1109/is3c.2014.135 2-s2.0-84904414248

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    Access to m-health-based system is denied to a large section of population because of their numbers, their incapacity to afford it, and lack of knowledge and skill to use it.

    “With regard to a large section of population, the access to m-health-based system is denied because of their numbers, their incapacity to afford it, and the lack of knowledge and skill to use it.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Fang et al. did a detailed survey on inference of computational methods in big data-based health informatics.

    “A detailed survey on the inference of computational methods in the big data-based health informatics has been done by Fang et al. in [30].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [30] Fang R. Pouyanfar S. Yang Y. Chen S.-C. Iyengar S. S. Computational health informatics in the big data age ACM Computing Surveys 2016 49 1 1 36 10.1145/2932707 2-s2.0-84975077478 source

      Not checked in the free run.

32 of 32

  • The paper relies on this without support.

    There is a deficiency of understanding the most suitable framework based on computational methodologies required for m-health.

    “However, there is a deficiency of understanding the most suitable framework based on the computational methodologies which are required for this approach.”
    “Still, there is a deficiency of understanding the most suitable framework based on the computational methodologies which are required for this approach.”

  • The paper relies on this without support.

    The main problems of big data analytics and m-health are yet to be solved.

    “The main problems of big data analytics and the m-health are yet to be solved.”

  • The paper relies on this without support.

    m-Health can be useful in monitoring health status and improving patient safety and quality of care.

    “m-Health could be a key element in healthcare systems [29] and can be useful in monitoring health status and improving patient safety and quality of care.”

  • The paper relies on this without support.

    Big data analytics can be used to identify abnormalities by analyzing a huge amount of data from various datasets and their sources.

    “These analytical approaches in big data can be used to identify the anomalies by analyzing a huge amount of data from various datasets and their sources.”

  • The paper relies on this without support.

    EHRs, EMRs, PHR, software for medical practice management, and various other components of healthcare data increase quality and efficiency of service and reduce overall cost of healthcare and medical errors.

    “EHRs, EMRs, PHR, software for the medical practice management, and various other components of the healthcare data increase the quality and efficiency of service and reduce the overall cost of healthcare and medical errors.”

  • The paper relies on this without support.

    Due to enhancement of mobile devices and wireless sensor networks, healthcare services were improved.

    “Due to the enhancement of mobile devices and wireless sensor networks, healthcare services were improved.”
6 of 6

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?

  • Wearable devices can significantly reduce healthcare costs by reducing the number of hospitalizations and emergency room visits.

    “In addition, they can significantly reduce healthcare costs by reducing the number of hospitalizations and emergency room visits, which can be expensive for both individuals and the healthcare system.”

    From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [14]

  • As biosensors are still in development stage for monitoring biochemical signals, the technology is yet to sense biological biomarkers continuously and needs higher sensitivity, selectivity, and accuracy towards specific biomolecule sensing.

    “As the biosensors are still in the development stage in monitoring biochemical signals, the technology is yet to sense biological biomarkers continuously, and they need to achieve higher sensitivity, selectivity, and accuracy towards specific biomolecule sensing [14,17].”

    From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [14]

  • One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare.

    “One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare.”

    From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [14]

  • Wearable technology enables remote monitoring of patients and personalized care.

    “Wearable technology enables remote monitoring of patients and personalized care.”

    From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [14]

  • Second-generation wearables must improve accuracy, battery life, data security, user adoption, and standardization.

    “The second-generation wearables must improve accuracy, battery life, data security, user adoption, and standardization [14,16].”

    From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [14]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

  • Exclusion categories fall 100 short of the reported total

    Minor

    The four stated exclusion categories sum to 2337 articles, not 2437.

    Reported 2437 · recomputed 2337

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 11

  1. 2015, mHealth sensors and applications for personal aid
  2. 2013, Brookings Institution Policy Report, 10, 1
  3. 2012, Harvard Business Review, 90, 60
  4. 2009, PLoS Medicine, 6
  5. 2017, Pharmacology & Therapeutics, 42, 572
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Authors
khan, Z. F., & Alotaibi, S. R.
Affiliations
Shaqra University (SA)
Corresponding author
khan, Z. Faizal
ORCID
Venue
Journal of Healthcare Engineering
Publisher
Wiley
Published
2020-09-01
Volume
2020
Pages
1-15
ISSN
  • 2040-2295 (ISSN-L)
  • 2040-2309
DOI
10.1155/2020/8894694
PMC
PMC7481991
OpenAlex
W3082150784
Semantic Scholar
e24e9050764d983616739f94e90bdd4012f0bade
Cited by
235
References
103
Citations by year
2026: 16 · 2025: 47 · 2024: 58 · 2023: 51 · 2022: 36 · 2021: 25 · 2020: 2
FWCI
4.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Healthcare Engineering (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
mobile health, artificial intelligence, big data analytics, health care system, electronic health records, medical images, healthcare resource planning
MeSH terms
Data Science, Big Data, Algorithms, Artificial Intelligence, Delivery of Health Care, Humans, Medical Informatics, Databases, Factual, Telemedicine, Biomedical Research, Cell Phone, Electronic Health Records

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

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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