ARTIFICIAL INTELLIGENCE IN PERSONALIZED OBESITY TREATMENT – OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.31435/ijitss.2(50).2026.5226Keywords:
Artificial Intelligence, Personalized Medicine, Obesity Treatment, Digital Health, Machine Learning, Predictive AnalyticsAbstract
Artificial Intelligence (AI) is increasingly becoming integral to the development of a strategies for obesity treatment, marking a notable shift within precision medicine and digital health. As obesity persists as a global public‑health challenge, conventional interventions often fail to account for substantial individual differences in physiology, behavior, and environmental exposure. Emerging AI‑based methods, including machine learning, predictive analytics, and digital phenotyping, enable the modeling of complex biological and behavioral patterns, thereby supporting more individualized approaches to prevention, diagnosis, and therapy.
This review provides an overview of contemporary AI applications in obesity management, with particular attention to intelligent dietary‑recommendation tools, adaptive monitoring of physical activity, and predictive models of weight‑loss outcomes. In addition, it discusses central ethical, practical, and regulatory issues, including data privacy, algorithmic transparency, and accessibility within healthcare systems. The paper concludes by identifying priority directions for future research, emphasizing the need for interdisciplinary collaboration, rigorous clinical validation, and equitable implementation frameworks to ensure the safe and effective integration of AI into personalized obesity care.
References
Agrawal, K., Goktas, P., Kumar, N., & Leung, M. F. (2025). Artificial intelligence in personalized nutrition and food manufacturing: A comprehensive review of methods, applications, and future directions. Frontiers in Nutrition, 12, Article 1636980. https://doi.org/10.3389/fnut.2024.1636980
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20, Article 310. https://doi.org/10.1186/s12911-020-01332-6
Ferdowsy, F., Rahi, K. S. A., Jabiullah, M. I., & Habib, M. T. (2021). A machine learning approach for obesity risk prediction. Current Research in Behavioral Sciences, 2, Article 100053. https://doi.org/10.1016/j.crbeha.2021.100053
Huang, L., Huhulea, E. N., Abraham, E., Bienenstock, R., Aifuwa, E., Hirani, R., ... Etienne, M. (2025). The role of artificial intelligence in obesity risk prediction and management: Approaches, insights, and recommendations. Medicina, 61(2), Article 358. https://doi.org/10.3390/medicina6100358
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024). Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 10(4), Article e25492. https://doi.org/10.1016/j.heliyon.2024.e25492
Morley, J., Machado, C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, Article 113172. https://doi.org/10.1016/j.socscimed.2020.113172
Obermeyer, Z., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Ordovas, J. M., Ferguson, L. R., Tai, E. S., & Mathers, J. C. (2020). Personalised nutrition and health. BMJ, 361, Article k2173. https://doi.org/10.1136/bmj.k2173
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., ... Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3, Article 119. https://doi.org/10.1038/s41746-020-00323-1
Samek, W., & Müller, K.-R. (2019). Towards explainable artificial intelligence in healthcare. Nature Biomedical Engineering, 3(11), 956–958. https://doi.org/10.1038/s41551-019-0401-3
Shaban, W. M., El-Din Moustafa, H., & El-Seddek, M. M. (2025). Machine learning framework for predicting susceptibility to obesity. Scientific Reports, 15, Article 35040. https://doi.org/10.1038/s41598-024-65039-4
Teke, J., Msiska, M., Adanini, O. A., Egbon, E., Osborne, A., & Olawade, D. B. (2025). Artificial intelligence for obesity management: A review of applications, opportunities, and challenges. Obesity Medicine, Article 100657. https://doi.org/10.1016/j.obmed.2025.100657
Torkamani, A., Andersen, K., Steinhubl, S. R., & Topol, E. J. (2018). High-definition medicine: The path toward precision health. Nature Medicine, 24(6), 804–810. https://doi.org/10.1038/s41591-018-0057-y
van de Vijver, S., Tensen, P., Asiki, G., Requena-Méndez, A., Heidenrijk, M., Stronks, K., ... Agyemang, C. (2023). Digital health for all: How digital health could reduce inequality and increase universal health coverage. Digital Health, 9, Article 20552076231185434. https://doi.org/10.1177/20552076231185434
Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., ... Segal, E. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001
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Copyright (c) 2026 Aleksandra Jagiełło, Piotr Pietrzyk, Beata Szreder, Patrycja Krawczyk, Joanna Ślusarczyk, Natalia Stanek, Maja Łapaj, Zuzanna Noweta, Milena Lewicka, Tamara Chodań
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