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Review
. 2020 Aug 30:2020:8894694.
doi: 10.1155/2020/8894694. eCollection 2020.

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

Z Faizal Khan  1 , Sultan Refa Alotaibi  1
Affiliations
Review

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

Z Faizal Khan et al. J Healthc Eng. .

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.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
PRISMA flowchart for the entire review process.
Figure 2
Figure 2
Schematic representation of the m-health scenario.
Figure 3
Figure 3
Global m-health markets [46].
Figure 4
Figure 4
Smartphone-based m-health model with AI and big data analytics.
Figure 5
Figure 5
Architecture of the proposed AI and big data analytics-based m-health system.
See this image and copyright information in PMC

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References

    1. Sousa P. S., Sabugueiro D., Felizardo V., Couto R., Pires I., Garcia N. M. mHealth sensors and applications for personal aid. In: Adibi S., editor. Mobile Health. Berlin, Germany: Springer; 2015. Springer Series in Bio-/Neuroinformatics vol 5.
    1. Majumder S., Deen M. J. Smartphone sensors for health monitoring and diagnosis. Sensors. 2019;19(9):p. 2164. doi: 10.3390/s19092164. - DOI - PMC - PubMed
    1. Darrell M. Improving health care through mobile medical devices and sensors. Brookings Institution Policy Report. 2013;10:1–13.
    1. Jamaladin H. Mobile apps for blood pressure monitoring: systematic search in app stores and content analysis. JMIR mHealth and uHealth. 2018;6(11) doi: 10.2196/mhealth.9888. - DOI - PMC - PubMed
    1. Jenny J.-Y., Bureggah A., Diesinger Y. Measurement of the knee flexion angle with smartphone applications: which technology is better? Knee Surgery, Sports Traumatology, Arthroscopy. 2016;24(9):2874–2877. doi: 10.1007/s00167-015-3537-4. - DOI - PubMed

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