ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE IN OBESITY MANAGEMENT: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS

Alicja Włodarczyk; Hubert Łagosz; Piotr Wites; Katarzyna Wites; Patryk Piotrowski; Aleksandra Jabłońska; Magdalena Pyzik; Kinga Ziółkowska; Karolina Markusiewicz; Piotr Bahyrycz

doi:10.31435/ijitss.3(51).2026.6101

Authors

Alicja Włodarczyk MD, Szpital Miejski Specjalistyczny im. Gabriela Narutowicza w Krakowie, Kraków, Poland https://orcid.org/0009-0009-9711-121X
Hubert Łagosz MD, 5 Wojskowy Szpital Kliniczny z Polikliniką, Kraków, Poland https://orcid.org/0009-0008-6635-2400
Piotr Wites Szpital Uniwersytecki w Krakowie, Kraków, Poland https://orcid.org/0000-0001-8234-5560
Katarzyna Wites Jagiellonian University Medical College, Kraków, Poland https://orcid.org/0009-0009-1796-0407
Patryk Piotrowski Samodzielny Publiczny Zakład Opieki Zdrowotnej w Myślenicach, Myślenice, Poland https://orcid.org/0009-0009-3795-3781
Aleksandra Jabłońska Jagiellonian University Medical College, Kraków, Poland https://orcid.org/0009-0004-0315-3771
Magdalena Pyzik Szpital św. Anny w Miechowie, Miechów, Poland https://orcid.org/0009-0000-5771-8935
Kinga Ziółkowska Medical University of Silesia, Katowice, Poland https://orcid.org/0009-0004-1695-4332
Karolina Markusiewicz Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0005-0273-386X
Piotr Bahyrycz Samodzielny Publiczny Zakład Opieki Zdrowotnej w Myślenicach, Myślenice, Poland https://orcid.org/0009-0009-7369-3366

DOI:

https://doi.org/10.31435/ijitss.3(51).2026.6101

Keywords:

Obesity, Artificial Intelligence, Precision Medicine, Machine Learning, Digital Health, Pharmacotherapy

Abstract

Obesity is a complex, chronic, and heterogeneous disease that affects more than one billion people worldwide and represents one of the greatest public health challenges of the twenty-first century. Despite significant advances in pharmacotherapy, including glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual incretin-based therapies, considerable interindividual variability in treatment response remains a major obstacle to effective obesity management. The emergence of precision medicine and artificial intelligence (AI) has created new opportunities to address this challenge by enabling individualized risk assessment, phenotype-based treatment selection, and prediction of therapeutic outcomes. Recent developments in genomics, machine learning, digital health technologies, and large-scale healthcare datasets have facilitated the transition from a one-size-fits-all approach toward personalized obesity care. AI-based models can integrate genetic, metabolic, behavioral, and clinical data to identify obesity subtypes, predict responses to pharmacological interventions, and support clinical decision-making. Furthermore, digital phenotyping and wearable technologies provide continuous monitoring of lifestyle behaviors and physiological parameters, allowing dynamic adaptation of treatment strategies. This review examines the current role of AI and precision medicine in obesity management, focusing on obesity phenotyping, genomic risk stratification, predictive analytics, digital health interventions, and AI-assisted pharmacotherapy selection. Additionally, ethical, regulatory, and implementation challenges are discussed. The integration of artificial intelligence with precision medicine has the potential to transform obesity treatment by improving therapeutic effectiveness, reducing healthcare costs, and advancing individualized patient care.

References

Acosta, A., Camilleri, M., Abu Dayyeh, B., Calderon, G., Gonzalez, D., McRae, A., Rossini, W., Singh, S., Burton, D., & Clark, M. M. (2021). Selection of antiobesity medications based on phenotypes enhances weight loss: A pragmatic trial in an obesity clinic. Obesity, 29(4), 662–671. https://doi.org/10.1002/oby.23120

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317–1318. https://doi.org/10.1001/jama.2017.18391

Blüher, M. (2019). Obesity: Global epidemiology and pathogenesis. Nature Reviews Endocrinology, 15(5), 288–298. https://doi.org/10.1038/s41574-019-0176-8

Bray, G. A., Kim, K. K., Wilding, J. P. H., & World Obesity Federation. (2017). Obesity: A chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obesity Reviews, 18(7), 715–723. https://doi.org/10.1111/obr.12551

Choong, C., Brnabic, A., Chinthammit, C., Ravuri, M., Terrell, K., & Kan, H. (2024). Applying machine learning approaches for predicting obesity risk using US health administrative claims database. BMJ Open Diabetes Research & Care, 12(5), e004193. https://doi.org/10.1136/bmjdrc-2024-004193

Clément, K., van den Akker, E., Argente, J., Bahm, A., Chung, W. K., Connors, H., De Waele, K., Farooqi, I. S., Gonneau-Lejeune, J., Gordon, G., Kohlsdorf, K., Poitou, C., Puder, L., Swain, J., Stewart, M., Yuan, G., Wabitsch, M., & Kühnen, P. (2020). Efficacy and safety of setmelanotide in individuals with severe obesity due to LEPR or POMC deficiency. The Lancet Diabetes & Endocrinology, 8(12), 960–970. https://doi.org/10.1016/S2213-8587(20)30364-8

Farooqi, I. S., & O'Rahilly, S. (2006). Genetics of obesity in humans. Endocrine Reviews, 27(7), 710–718. https://doi.org/10.1210/er.2006-0040

Jastreboff, A. M., Aronne, L. J., Ahmad, N. N., Wharton, S., Connery, L., Alves, B., Kiyosue, A., Zhang, S., Liu, B., Bunck, M. C., Stefanski, A., & SURMOUNT-1 Investigators. (2022). Tirzepatide once weekly for the treatment of obesity. The New England Journal of Medicine, 387(3), 205–216. https://doi.org/10.1056/NEJMoa2206038

Jastreboff, A. M., Kaplan, L. M., Frías, J. P., Wu, Q., Du, Y., Gurbuz, S., Coskun, T., Haupt, A., Milicevic, Z., Hartman, M. L., & Retatrutide Phase 2 Obesity Trial Investigators. (2023). Triple-hormone-receptor agonist retatrutide for obesity: A phase 2 trial. The New England Journal of Medicine, 389(6), 514–526. https://doi.org/10.1056/NEJMoa2301972

Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., & Kathiresan, S. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics, 51(8), 1219–1224. https://doi.org/10.1038/s41588-018-0183-z

Kühnen, P., Clément, K., Wiegand, S., Blankenstein, O., Gottesdiener, K., Martini, L. L., Mai, K., Blume-Peytavi, U., Grüters, A., & Krude, H. (2016). Proopiomelanocortin deficiency treated with a melanocortin-4 receptor agonist. The New England Journal of Medicine, 375(3), 240–246. https://doi.org/10.1056/NEJMoa1512693

Loos, R. J. F., & Yeo, G. S. H. (2022). The genetics of obesity: From discovery to biology. Nature Reviews Genetics, 23(2), 120–133. https://doi.org/10.1038/s41576-021-00414-z

Patel, M. L., Wakayama, L. N., & Bennett, G. G. (2021). Self-monitoring via digital health in weight loss interventions: A systematic review among adults with overweight or obesity. Obesity, 29(3), 478–499. https://doi.org/10.1002/oby.23088

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

Volkow, N. D., Wang, G. J., Tomasi, D., & Baler, R. D. (2013). Obesity and addiction: Neurobiological overlaps. Obesity Reviews, 14(1), 2–18. https://doi.org/10.1111/j.1467-789X.2012.01031.x

Wang, G. J., Volkow, N. D., Logan, J., Pappas, N. R., Wong, C. T., Zhu, W., Netusil, N., & Fowler, J. S. (2001). Brain dopamine and obesity. The Lancet, 357(9253), 354–357. https://doi.org/10.1016/S0140-6736(00)03643-6

Wilding, J. P. H., Batterham, R. L., Calanna, S., Davies, M., Van Gaal, L. F., Lingvay, I., McGowan, B. M., Rosenstock, J., Tran, M. T. D., Wadden, T. A., Wharton, S., Yokote, K., Zeuthen, N., Kushner, R. F., & STEP 1 Study Group. (2021). Once-weekly semaglutide in adults with overweight or obesity. The New England Journal of Medicine, 384(11), 989–1002. https://doi.org/10.1056/NEJMoa2032183