The role of artificial intelligence and machine learning in digital health and telemedicine ecosystems: A comprehensive review

Authors

https://doi.org/10.22105/thi.vi.33

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have become essential technological drivers in digital health and telemedicine, enabling more efficient clinical workflows, improved diagnostic accuracy, and optimized remote care delivery. With the growing adoption of Remote Patient Monitoring (RPM), Mobile Health (mHealth), wearable devices, and Internet of Medical Things (IoMT) ecosystems, AI-driven systems now play a crucial role in predictive analytics, personalized care, and intelligent clinical decision-making. This review provides an in-depth analysis of emerging AI/ML-powered digital health applications, technological advancements, clinical benefits, implementation challenges, and future opportunities. The paper synthesizes recent findings (2019–2025) from leading scientific literature and highlights the impact of AI on telehealth interventions, smart hospitals, digital therapeutics, virtual care, and Health Information Systems (HISs). It further explores interoperability issues, privacy and security concerns, blockchain-based healthcare models, and policy implications for the next generation of digital health ecosystems.

Keywords:

Artificial intelligence, Machine learning, Digital health, Telemedicine, Remote patient monitoring

References

  1. [1] Wosik, J., Fudim, M., Cameron, B., Gellad, Z. F., Cho, A., Phinney, D., ... & Tcheng, J. (2020). Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association, 27(6), 957–962. https://doi.org/10.1093/jamia/ocaa067
  2. [2] Sim, J. Z. T., Fong, Q. W., Huang, W., & Tan, C. H. (2021). Machine learning in medicine: What clinicians should know. Singapore Medical Journal, 64(2), 91. https://doi.org/10.11622/smedj.2021054
  3. [3] Ding, M., Qu, A., Zhong, H., & Liang, H. (2021). A transformer-based network for pathology image classification. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2028–2034). IEEE. https://doi.org/10.1109/BIBM52615.2021.9669476
  4. [4] Yu, K. H., & Kohane, I. S. (2019). Framing the challenges of artificial intelligence in medicine. BMJ Quality & Safety, 28(3), 238–241. https://doi.org/10.1136/bmjqs-2018-008551
  5. [5] McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://www.nature.com/articles/s41586-019-1799-6
  6. [6] Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://www.nature.com/articles/s41591-018-0300-7
  7. [7] Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82. https://doi.org/10.1056/NEJMp2005835
  8. [8] Van Buchem, M. M., Boosman, H., Bauer, M. P., Kant, I. M. J., Cammel, S. A., & Steyerberg, E. W. (2021). The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digital Medicine, 4(1), 57. https://www.nature.com/articles/s41746-021-00432-5
  9. [9] Rossi, M., & Rehman, S. (2025). Integrating artificial intelligence into telemedicine: evidence, challenges, and future directions. Cureus, 17(8), e90829. https://doi.org/10.7759/cureus.90829
  10. [10] Golinelli, D., Boetto, E., Carullo, G., Nuzzolese, A. G., Landini, M. P., & Fantini, M. P. (2020). Adoption of digital technologies in health care during the COVID-19 pandemic: Systematic review of early scientific literature. Journal of Medical Internet Research, 22(11), e22280. https://doi.org/10.2196/22280
  11. [11] Bokolo, A. J. (2021). Exploring the adoption of telemedicine and virtual software for care of outpatients during and after COVID-19 pandemic. Irish Journal of Medical Science (1971-), 190(1), 1–10. https://doi.org/10.1007/s11845-020-02299-z
  12. [12] Contreras, C. M., Metzger, G. A., Beane, J. D., Dedhia, P. H., Ejaz, A., & Pawlik, T. M. (2020). Telemedicine: Patient-provider clinical engagement during the COVID-19 pandemic and beyond. Journal of Gastrointestinal Surgery, 24(7), 1692–1697. https://doi.org/10.1007/s11605-020-04623-5
  13. [13] Messner, E. M., Probst, T., O’Rourke, T., Stoyanov, S., & Baumeister, H. (2019). mHealth applications: Potentials, limitations, current quality and future directions. In Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics (pp. 235–248). Springer. https://doi.org/10.1007/978-3-030-31620-4_15
  14. [14] Peyroteo, M., Ferreira, I. A., Elvas, L. B., Ferreira, J. C., & Lapão, L. V. (2021). Remote monitoring systems for patients with chronic diseases in primary health care: Systematic review. JMIR Mhealth and Uhealth, 9(12), e28285. https://doi.org/10.2196/28285
  15. [15] Devine, K. A., Viola, A. S., Coups, E. J., & Wu, Y. P. (2018). Digital health interventions for adolescent and young adult cancer survivors. JCO Clinical Cancer Informatics, 2, 1–15. https://doi.org/10.1200/CCI.17.00138
  16. [16] Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., ... & Turakhia, M. P. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909–1917. https://doi.org/10.1056/NEJMoa1901183
  17. [17] Annis, T., Pleasants, S., Hultman, G., Lindemann, E., Thompson, J. A., Billecke, S., … & Melton, G. B. (2020). Rapid implementation of a COVID-19 remote patient monitoring program. Journal of the American Medical Informatics Association, 27(8), 1326–1330. https://doi.org/10.1093/jamia/ocaa097
  18. [18] Yuan, S., Yang, Z., Li, J., Wu, C., & Liu, S. (2025). AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: A meta-analysis. BMC Medical Informatics and Decision Making, 25(1), 203. https://doi.org/10.1186/s12911-025-03048-x
  19. [19] Singh, B., Chastin, S., Miatke, A., Curtis, R., Dumuid, D., Brinsley, J., ... & Maher, C. (2024). Real-world accuracy of wearable activity trackers for detecting medical conditions: Systematic review and meta-analysis. JMIR Mhealth and Uhealth, 12, e56972. https://doi.org/10.2196/56972
  20. [20] Adler-Milstein, J., & Jha, A. K. (2017). HITECH Act drove large gains in hospital electronic health record adoption. Health Affairs, 36(8), 1416–1422. https://doi.org/10.1377/hlthaff.2016.1651
  21. [21] Dinh-Le, C., Chuang, R., Chokshi, S., & Mann, D. (2019). Wearable health technology and electronic health record integration: Scoping review and future directions. JMIR Mhealth and Uhealth, 7(9), e12861. https://doi.org/10.2196/12861
  22. [22] Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. C. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health, 2(8), e435--e440. https://doi.org/10.1016/S2589-7500(20)30142-4
  23. [23] Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2015). The emerging field of mobile health. Science Translational Medicine, 7(283), 283rv3--283rv3. https://doi.org/10.1126/scitranslmed.aaa3487
  24. [24] Noah, B., Keller, M. S., Mosadeghi, S., Stein, L., Johl, S., Delshad, S., ... & Spiegel, B. M. (2018). Impact of remote patient monitoring on clinical outcomes: An updated meta-analysis of randomized controlled trials. NPJ Digital Medicine, 1(1), 20172. https://www.nature.com/articles/s41746-017-0002-4
  25. [25] Alhammad, N., Alajlani, M., Abd-Alrazaq, A., Epiphaniou, G., & Arvanitis, T. (2024). Patients’ perspectives on the data confidentiality, privacy, and security of mHealth apps: Systematic review. Journal of Medical Internet Research, 26, e50715. https://doi.org/10.2196/50715
  26. [26] Hwang, E. J., Park, S., Jin, K. N., Kim, J. I., Choi, S. Y., Lee, J. H., ... & Park, C. M. (2019). Development and validation of a deep learning--based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Network Open, 2(3), e191095. https://doi.org/10.1001/jamanetworkopen.2019.1095
  27. [27] Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019). Automated triaging of adult chest radiographs with deep artificial neural networks. Radiology, 291(1), 196–202. https://doi.org/10.1148/radiol.2018180921
  28. [28] Wang, M., Sun, Z., Jia, M., Wang, Y., Wang, H., Zhu, X., … & Ji, H. (2022). Intelligent virtual case learning system based on real medical records and natural language processing. BMC Medical Informatics and Decision Making, 22(1), 60. https://doi.org/10.1186/s12911-022-01797-7
  29. [29] Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., … & Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(8), 1301–1309. https://www.nature.com/articles/s41591-019-0508-1
  30. [30] Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M. D., Van der Laak, J., Bui, M. M., ... & Kozlowski, C. (2019). Computational pathology definitions, best practices, and recommendations for regulatory guidance: A white paper from the Digital Pathology Association. The Journal of Pathology, 249(3), 286–294. https://doi.org/10.1002/path.5331
  31. [31] McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., … & Treanor, D. (2024). Artificial intelligence in digital pathology: A systematic review and meta-analysis of diagnostic test accuracy. NPJ Digital Medicine, 7(1), 114. https://www.nature.com/articles/s41746-024-01106-8
  32. [32] Bulten, W., Pinckaers, H., Van Boven, H., Vink, R., De Bel, T., Van Ginneken, B., … & Litjens, G. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study. The Lancet Oncology, 21(2), 233–241. https://doi.org/10.1016/S1470-2045(19)30739-9
  33. [33] Cao, S., Zhang, R., Jiang, A., Kuerban, M., Wumaier, A., Wu, J., ... & Chen, R. (2023). Application effect of an artificial intelligence-based fundus screening system: Evaluation in a clinical setting and population screening. BioMedical Engineering Online, 22(1), 38. https://doi.org/10.1186/s12938-023-01097-9
  34. [34] Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., … & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
  35. [35] Smak Gregoor, A. M., Sangers, T. E., Bakker, L. J., Hollestein, L., Uyl--de Groot, C. A., Nijsten, T., & Wakkee, M. (2023). An artificial intelligence based app for skin cancer detection evaluated in a population based setting. NPJ Digital Medicine, 6(1), 90. https://doi.org/10.1038/s41746-023-00831-w
  36. [36] Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., & Ng, A. Y. (2017). Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks. https://arxiv.org/abs/1707.01836
  37. [37] Hao, J., Kwapong, W. R., Shen, T., Fu, H., Xu, Y., Lu, Q., ... & Zhao, Y. (2024). Early detection of dementia through retinal imaging and trustworthy AI. NPJ Digital Medicine, 7(1), 294. https://doi.org/10.1038/s41746-024-01292-5
  38. [38] Shimabukuro, D. W., Barton, C. W., Feldman, M. D., Mataraso, S. J., & Das, R. (2017). Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: A randomised clinical trial. BMJ Open Respiratory Research, 4(1), e000234. https://doi.org/10.1136/bmjresp-2017-000234
  39. [39] Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16(11), 703–715. https://www.nature.com/articles/s41571-019-0252-y
  40. [40] Miner, A. S., Milstein, A., Schueller, S., Hegde, R., Mangurian, C., & Linos, E. (2016). Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Internal Medicine, 176(5), 619–625. https://doi.org/10.1001/jamainternmed.2016.0400
  41. [41] Sasseville, M., Yousefi, F., Ouellet, S., Naye, F., Stefan, T., Carnovale, V., ... & LeBlanc, A. (2025). The impact of ai scribes on streamlining clinical documentation: A systematic review. Healthcare (Vol. 13, p. 1447). MDPI. https://doi.org/10.3390/healthcare13121447

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Published

2026-03-09

Issue

Vol. 3 No. 1 (2026): Trends in Health Informatics

Section

Articles

How to Cite

Ghanbari, R. . (2026). The role of artificial intelligence and machine learning in digital health and telemedicine ecosystems: A comprehensive review. Trends in Health Informatics, 3(1), 12-21. https://doi.org/10.22105/thi.vi.33

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