Editorial: Artificial Intelligence in Healthcare and Wellness — Emerging Frameworks, Intelligent Monitoring, and Precision Medicine
Article Sidebar
Main Article Content
Abstract
AI is changing our understanding of health and health-care systems in ways that go beyond the traditional one-click-fits-all approach to care. It is also making care more personalised than ever before, with delivery occurring on an ongoing basis and at all times. The six papers in this special issue of the Journal of Informatics and Web Engineering will give you a clear view of where we are headed. They examine several different areas, including: monitoring chronic illness through wearable technology; using deep learning techniques for contactless heart monitoring via rPPG; developing new cancer risk models; identifying neuroplasticity therapeutic pathways using multi-agent systems; generating synthetic health data; and developing new tri-domain frameworks to examine the interrelationships among the heart, brain, and body. Each of these papers addresses some aspect of a true issue, such as lack of data, limited access, and clinical challenges that traditional methods cannot solve. Collectively, the studies presented in this issue do not just point toward new revolutionary computing; they also provide insight into the future of the health care system. A more intelligent health care system that can adapt to its environment; respond to its client, and ground itself in both ethical and clinical realities. This editorial will outline the common themes that are found throughout the contributions in this special issue. The editorial is an integrative perspective across multiple fields including real-time physiological sensing, explainability and trust, data scarcity solutions, and holistic AI wellness model development.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles published in JIWE are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License. Readers are allowed to
- Share — copy and redistribute the material in any medium or format under the following conditions:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use;
- NonCommercial — You may not use the material for commercial purposes;
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
References
T. Davenport and R. Kalakota, “The potential for artificial intelligence in healthcare,” Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, Jun. 2019, doi: 10.7861/futurehosp.6-2-94.
E. J. Topol, “High-performance medicine: the convergence of human and artificial intelligence,” Nature Medicine, vol. 25, no. 1, pp. 44–56, Dec. 2018, doi: 10.1038/s41591-018-0300-7.
J. Bajwa, U. Munir, A. Nori, and B. Williams, “Artificial intelligence in healthcare: transforming the practice of medicine,” Future Healthcare Journal, vol. 8, no. 2, pp. e188–e194, Jul. 2021, doi: 10.7861/fhj.2021-0095.
World Health Organization, "Noncommunicable diseases: Key facts," WHO, 2023.
P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “AI in health and medicine,” Nature Medicine, vol. 28, no. 1, pp. 31–38, Jan. 2022, doi: 10.1038/s41591-021-01614-0.
S. A. Alowais et al., “Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC Medical Education, vol. 23, no. 1, p. 689, Sep. 2023, doi: 10.1186/s12909-023-04698-z.
A.. Thacharodi et al., “Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future—A comprehensive review,” Health Care Science, vol. 3, no. 5, pp. 329–349, Oct. 2024, doi: 10.1002/hcs2.115.
Y. A. Fahim, I. W. Hasani, S. Kabba, and W. M. Ragab, “Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives,” European Journal of Medical Research, vol. 30, no. 1, p. 848, Sep. 2025, doi: 10.1186/s40001-025-03196-w.
Y. Daineko and A. Seitnur, “Artificial intelligence for Predictive health analytics: challenges, methods, and immersive system integration,” Procedia Computer Science, vol. 265, pp. 636–641, Jan. 2025, doi: 10.1016/j.procs.2025.07.232.
P. L. Chong et al., “Integrating artificial intelligence in healthcare: applications, challenges, and future directions,” Future Science OA, vol. 11, no. 1, p. 2527505, Jul. 2025, doi: 10.1080/20565623.2025.2527505.
Y.-H. Li, Y.-L. Li, M.-Y. Wei, and G.-Y. Li, “Innovation and challenges of artificial intelligence technology in personalized healthcare,” Scientific Reports, vol. 14, no. 1, p. 18994, Aug. 2024, doi: 10.1038/s41598-024-70073-7.
D. C. Angus et al., “AI, health, and health care today and tomorrow,” JAMA, vol. 334, no. 18, p. 1650, Oct. 2025, doi: 10.1001/jama.2025.18490.
C. Silcox et al., “The potential for artificial intelligence to transform healthcare: perspectives from international health leaders,” Npj Digital Medicine, vol. 7, no. 1, p. 88, Apr. 2024, doi: 10.1038/s41746-024-01097-6.
R. Hirani et al., “Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities,” Life, vol. 14, no. 5, p. 557, Apr. 2024, doi: 10.3390/life14050557.
A. A. Kuwaiti et al., “A review of the role of artificial intelligence in healthcare,” Journal of Personalized Medicine, vol. 13, no. 6, p. 951, Jun. 2023, doi: 10.3390/jpm13060951.
S. Yelne, M. Chaudhary, K. Dod, A. Sayyad, and R. Sharma, “Harnessing the Power of AI: A Comprehensive review of its impact and challenges in nursing science and healthcare,” Cureus, vol. 15, no. 11, p. e49252, Nov. 2023, doi: 10.7759/cureus.49252.
A. Mohajer-Bastami et al., “Artificial intelligence in healthcare: applications, challenges, and future directions. A narrative review informed by international, multidisciplinary expertise,” Frontiers in Digital Health, vol. 7, p. 1644041, Nov. 2025, doi: 10.3389/fdgth.2025.1644041.
A. S. George and A. S. H. George, “Transforming Healthcare with Artificial Intelligence: Strategies, Insights, and Frameworks for Innovation,” Zenodo (CERN European Organization for Nuclear Research), Jun. 2025, doi: 10.5281/zenodo.15685381.
M. Bekbolatova, J. Mayer, C. W. Ong, and M. Toma, "Transformative potential of AI in healthcare: definitions, applications, and navigating the ethical landscape and public perspectives," Healthcare (Basel), vol. 12, no. 2, p. 125, Jan. 2024. doi: 10.3390/healthcare12020125
C. Mennella, U. Maniscalco, G. De Pietro, and M. Esposito, "Ethical and regulatory challenges of AI technologies in healthcare: A narrative review," Heliyon, vol. 10, no. 4, p. e26297, 2024. doi: 10.1016/j.heliyon.2024.e26297
A. L. Silveira, R. Da Rosa Righi, and C. A. Da Costa, “Multi-agent systems for clinical decision support: A systematic review,” Applied Soft Computing, vol. 188, p. 114447, Dec. 2025, doi: 10.1016/j.asoc.2025.114447.
R. Elkamouchi, A. Daaif, and K. Elguemmat, “Multi-Agents System in Healthcare: A Systematic Literature review,” in Communications in computer and information science, 2024, pp. 200–214. doi: 10.1007/978-3-031-77043-2_16.
X. Liu, J. Fromm, S. Patel, and D. McDuff, “Multi-Task temporal shift attention networks for On-Device contactless vitals measurement,” arXiv (Cornell University), Jun. 2020, doi: 10.48550/arxiv.2006.03790
B. Njei et al., “Artificial intelligence agents in healthcare research: A scoping review,” PLoS ONE, vol. 21, no. 2, p. e0342182, Feb. 2026, doi: 10.1371/journal.pone.0342182.
C. Liu et al., “Multimodal wearable biosensing meets multidomain AI: a pathway to decentralized healthcare,” Advanced Science, p. e22900, Feb. 2026, doi: 10.1002/advs.202522900.
S. Palaniappan, R. Logeswaran, K. Subaramaniam, O. Baker, and N. D. Bui, “Training the brain: a machine learning approach to predicting wellbeing through intentional thought pattern modification,” Journal of Informatics and Web Engineering, vol. 4, no. 3, pp. 64–89, Oct. 2025, doi: 10.33093/jiwe.2025.4.3.4.
S.-C. Haw, J. Jayaram, and E. Anaam, “A hybrid recommender model for health supplements in e-commerce,” AIP Conference Proceedings, vol. 3367, p. 030009, Jan. 2025, doi: 10.1063/5.0299259.
S. Baluta, V. Suresh, M. Chmielowska, L. Smeesters, and J. Cabaj, “Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies,” ACS Omega, vol. 11, no. 1, pp. 82–99, Jan. 2026, doi: 10.1021/acsomega.5c06072.
R. Chandrasekaran and E. Moustakas, “Patient attitudes toward ambient artificial intelligence scribes in clinical care: insights from a cross-sectional study,” Journal of the American Medical Informatics Association, vol. 33, no. 2, pp. 263–272, Nov. 2025, doi: 10.1093/jamia/ocaf218.
A. Arora et al., “The value of standards for health datasets in artificial intelligence-based applications,” Nature Medicine, vol. 29, no. 11, pp. 2929–2938, Oct. 2023, doi: 10.1038/s41591-023-02608-w.
C. Kim, S. U. Gadgil, and S.-I. Lee, “Transparency of medical artificial intelligence systems,” Nature Reviews Bioengineering, vol. 4, no. 1, pp. 11–29, Sep. 2025, doi: 10.1038/s44222-025-00363-w.
P. Naveen, S.C. Haw, D. Nadthan, S.K. Ramamoorthy, “Improving Chatbot Performance using Hybrid Deep Learning Approach,” Journal of System and Management Sciences, vol. 13, no. 3, May 2023, doi: 10.33168/jsms.2023.0334.