APPLICATION OF ARTIFICIAL INTELLIGENCE IN MOBILE HEALTH CARE APPLICATIONS: A SCOPING REVIEW

Authors

  • Ibrahim Anka Salihu
    Nile University of Nigeria image/svg+xml
  • Asmau Usman
    Nile University of Nigeria image/svg+xml
  • Hauwa Ahmad Amshi
    Federal University, Gashua
  • Aminu Aminu Muazu
    Umaru Musa Yar’adua University Katsina
  • Bashir Aliyu Sani
    Abdu Gusau Polytechnic
  • Zaharadden Sani
    Federal University Dutsin-Ma image/svg+xml

Keywords:

Mobile App, Android, Health, Artificial Intelligence

Abstract

The integration of artificial intelligence (AI) with mobile health (mHealth) applications has transformed the healthcare landscape by offering early disease detection, accessible, real-time, and personalized medical support. This review examines the current state and trends of AI-powered mobile health applications, examining their implementation across various medical domains such as mental health, chronic disease management, visual impairment, stroke rehabilitation, and fitness. A comprehensive analysis of peer-reviewed literature from 2015 to 2025 was conducted, focusing on the types of AI algorithms used, their target applications, and overall effectiveness. The findings reveal an increasing trend in scholarly interest, particularly in journal publications, reflecting the demand for validated and reliable AI health solutions. Neural networks and deep learning models dominate the algorithmic landscape due to their effectiveness in handling complex and unstructured health data. This study highlights the growing reception and potential of AI-integrated mHealth apps to revolutionize personal healthcare and surface the way for more intelligent, user-centric solutions.

Dimensions

Salihu, I.A., et al., AMOGA: A static-dynamic model generation strategy for mobile apps testing. IEEE Access, 2019. 7: p. 17158-17173.

Patrick, K., et al., The pace of technologic change: implications for digital health behavior intervention research. 2016, Elsevier. p. 816-824. https://doi.org/10.1016/j.amepre.2016.05.001

Cedeno-Moreno, D. and M. Vargas-Lombardo, Mobile Applications for Diabetes Self-Care and Approach to Machine Learning. 2020. https://www.learntechlib.org/p/218010/.

Usman, A., N. Ibrahim, and I.A. Salihu, TEGDroid: Test case generation approach for android apps considering context and GUI events. International Journal on Advanced Science, Engineering and Information Technology, 2020. 10(1): p. 16.

Pascoe, L. and J.W. Mwangoka, A smartphone-based reporting application for routine health data: System requirements, analysis and design. International Journal of Telemedicine and Clinical Practices, 2016. 1(4): p. 323-344. https://doi.org/10.1504/IJTMCP.2016.078429

Pearce, G., et al. Artificial neural network and mobile applications in medical diagnosis. in 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim). 2015. IEEE. DOI: https://doi.org/10.1109/UKSim.2015.34

Usman, A., et al., Test Case Generation Approach for Android Applications using Reinforcement Learning. Engineering, Technology & Applied Science Research, 2024. 14(4): p. 15127-15132. https://doi.org/10.48084/etasr.7422

Jimnez-Serrano, S., S. Tortajada, and J.M. Garca-Gmez, A mobile health application to predict postpartum depression based on machine learning. Telemedicine and e-Health, 2015. 21(7): p. 567-574. https://doi.org/10.1089/tmj.2014.0113

Awad, M., et al. Intelligent eye: A mobile application for assisting blind people. in 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM). 2018. IEEE. DOI: https://doi.org/10.1109/MENACOMM.2018.8371005

Salihu, I.A., et al. Mutation Testing Techniques for Android Applications: A Comparative Study. in 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). 2023. IEEE.

Siristatidis, C., et al., Mobile apps for helping patient-users: is it still far-fetched? Sustainability, 2019. 12(1): p. 106. https://doi.org/10.3390/su12010106

Hendrick, H., et al. iOS mobile APP for tuberculosis detection based on chest X-ray image. in 2019 2nd International Conference on Applied Information Technology and Innovation (ICAITI). 2019. IEEE. DOI: https://doi.org/10.1109/ICAITI48442.2019.8982152

Mdhaffar, A., et al. DL4DED: Deep learning for depressive episode detection on mobile devices. in How AI Impacts Urban Living and Public Health: 17th International Conference, ICOST 2019, New York City, NY, USA, October 14-16, 2019, Proceedings 17. 2019. Springer International Publishing. https://doi.org/10.1007/978-3-030-32785-9_10

Theilig, M.-M., et al., Employing environmental data and machine learning to improve mobile health receptivity. IEEE Access, 2019. 7: p. 179823-179841. https://doi.org/10.1109/ACCESS.2019.2958474

Talab, E., et al. Detecting heart anomalies using mobile phones and machine learning. in 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). 2019. IEEE. DOI: https://doi.org/10.1109/BIBE.2019.00083

Usman, A., et al., An In-Depth Analysis of Machine Learning Based Techniques for Automated Testing of Android Applications. International Journal of Communication Networks and Information Security, 2024. 16(3): p. 1-20.

Khaled, N., et al. In-door assistant mobile application using cnn and tensorflow. in 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). 2020. IEEE. DOI: 10.1109/ICECCE49384.2020.9179386

Gamble, A., Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective. Aslib Journal of Information Management, 2020. 72(4): p. 509-523. https://doi.org/10.1108/AJIM-11-2019-0316

Aljojo, N., et al., Alzheimer assistant: a mobile application using Machine Learning. Romanian Journal of Information Technology and Automatic Control, 2020. 30(4): p. 7-26. https://doi.org/10.33436/v30i4y202001

Caballero, A.R., K.E.I. Catli, and A.G.F. Babierra. Object Recognition and Hearing Assistive Technology Mobile Application using Convolutional Neural Network. in Proceedings of the 2020 International Conference on Wireless Communication and Sensor Networks. 2020. https://doi.org/10.1145/3411201.3411208

Usman, A., et al. Reinforcement Learning for Testing Android Applications: A Review. in 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). 2023. IEEE.

Afrah, I.A. and U. Kose, MHealth: An Artificial Intelligence Oriented Mobile Application for Personal Healthcare Support. arXiv preprint arXiv:2108.09277, 2021. https://doi.org/10.48550/arXiv.2108.09277

Tharushika, G.A., et al. Machine Learning-Based Skin and Heart Disease Diagnose Mobile App. in 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). 2021. IEEE. DOI: https://doi.org/10.1109/ECAI52376.2021.9515126

Salihu, I.A., R. Ibrahim, and A. Usman. A Static-dynamic Approach for UI Model Generation for Mobile Applications. in 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). 2018. IEEE.

Amugongo, L.M., et al., Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications. AI and Ethics, 2023: p. 1-18. https://doi.org/10.1007/s43681-023-00331-3

Labeeshan, A., et al. iretina: An intelligent mobile application for the visually impaired in an indoor environment. in 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS). 2021. IEEE. DOI: https://doi.org/10.1109/ICIIS53135.2021.9660748.

Usman, A., N. Ibrahim, and I.A. Salihu, Comparative study of mobile applications testing techniques for context events. Advanced Science Letters, 2018. 24(10): p. 7305-7310.

Pascucci, M., et al., AI-based mobile application to fight antibiotic resistance. Nature communications, 2021. 12(1): p. 1173. doi: https://doi.org/10.1038/s41467-021-21187-3.

Sefa-Yeboah, S.M., et al., Development of a mobile application platform for self-management of obesity using artificial intelligence techniques. International Journal of Telemedicine and Applications, 2021. 2021. https://doi.org/10.1155/2021/6624057

Elbagoury, B.M., et al. Mobile AI Stroke Health App: A Novel Mobile Intelligent Edge Computing Engine based on Deep Learning models for Stroke PredictionResearch and Industry Perspective. in 2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). 2021. IEEE. DOI: 10.1109/ICCICC53683.2021.9811307

Wichmann, R.M., et al., Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study. Plos one, 2022. 17(12): p. e0278397. https://doi.org/10.1371/journal.pone.0278397

Triana, Y.S., et al., Neural network models selection scheme for health mobile app development. IAES International Journal of Artificial Intelligence, 2023. 12(3): p. 1191-1203. https://doi.org/10.11591/ijai.v12.i3.pp1191-1203

Das, U.C., et al. Stroke Home Rehabilitation Approach Using Mobile Application Based on PostNet Machine Learning Model. in Proceedings of the 2023 7th International Conference on Medical and Health Informatics. 2023.https://doi.org/10.1145/3608298.3608339

Wijekoon, W.D.H. and S. Harshanath, Meal Preparation Algorithm for Diabetic Patients Using Machine Learning. Sri Lankan Journal of Applied Sciences, 2023. 1(02): p. 27-33.https://sljoas.uwu.ac.lk/index.php/sljoas/article/view/51

Lamem, M. F. H., Sahid, M. I., & Ahmed, A. (2025). Artificial intelligence for access to primary healthcare in rural settings. Journal of Medicine, Surgery, and Public Health, 5, 100173.

Usman, A., N. Ibrahim, and I.A. Salihu. Test case generation from android mobile applications focusing on context events. in Proceedings of the 2018 7th International Conference on Software and Computer Applications. 2018.

Yong, M. T. T., Ho, S. B., & Tan, C. H. (2025). Migraine generative artificial intelligence based on mobile personalized healthcare. Journal of Informatics and Web Engineering, 4(1), 275-291.

Published

14-07-2025

How to Cite

APPLICATION OF ARTIFICIAL INTELLIGENCE IN MOBILE HEALTH CARE APPLICATIONS: A SCOPING REVIEW. (2025). FUDMA JOURNAL OF SCIENCES, 9(7), 89-95. https://doi.org/10.33003/fjs-2025-0907-3624

Issue

Vol. 9 No. 7 (2025): FUDMA Journal of Sciences - Vol. 9 No. 7

Section

Review Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

APPLICATION OF ARTIFICIAL INTELLIGENCE IN MOBILE HEALTH CARE APPLICATIONS: A SCOPING REVIEW. (2025). FUDMA JOURNAL OF SCIENCES, 9(7), 89-95. https://doi.org/10.33003/fjs-2025-0907-3624