Artificial intelligence in the diagnosis and prognosis of multimorbidity in the elderly

Aim. To evaluate the effectiveness of artificial intelligence in diagnosing and predicting multimorbidity in people over 65 years based on current literature data.

Materials and methods. A systematic review of 153 studies from January 1, 2020 to March 1, 2025 was conducted following PRISMA 2020 guidelines. The PICO model was applied: population – elderly people with multimorbidity (two or more chronic conditions), intervention – artificial intelligence tools (machine learning, deep learning), outcomes – diagnostic accuracy and prognostic performance. Keyword searches were performed in PubMed, Scopus, Web of Science, and Google Scholar databases. Data were synthesized narratively and quantitatively via meta-analysis using the R software version 4.3.2. The method excels in detecting hidden patterns compared to clinical scales.

Results. Artificial intelligence demonstrated high diagnostic accuracy for dementia (AUC = 0.833), stroke (AUC = 0.91), cardiovascular diseases (AUC = 0.986–0.991), and osteoporosis (AUC= 0.972). Prognostic performance reached AUC ≈ 0.87 (95% confidence interval: 0.83–0.91) for mortality and hospitalizations. However, for multimorbidity, accuracy was lower (AUC = 0.787– 0.93) due to data heterogeneity and the complexity of disease interactions.

Conclusion. Artificial intelligence enhances diagnostic and prognostic capabilities in geriatrics, particularly for individual conditions, but requires data standardization and dynamic models for multimorbidity. Challenges, such as digital ageism and data quality, still hinder its implementation.

1. United Nations, Department of Economic and Social Affairs, Population Division. World Population Ageing 2020 Highlights. New York: UN; 2020:45.

2. Fortin M., Stewart M., Poitras M.E., Almirall J., Maddocks H. A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann. Fam. Med. 2012;10(2):142–151. DOI: 10.1370/afm.1337.

3. Abadir P., Chellappa R. Artificial intelligence in geriatrics: riding the inevitable tide of promise, challenges, and considerations. J. Gerontol. A Biol. Sci. Med. Sci. 2024;79(2):glad279. DOI: 10.1093/gerona/glad279.

4. Topol E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019;25(1):44–56. DOI: 10.1038/s41591-018-0300-7.

5. Liu X., Faes L., Kale A.U., Wagner S.K., Fu D.J., Bruynseels A. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health. 2019;1(6):e271–e297. DOI: 10.1016/S2589-7500(19)30123-2.

6. Rajkomar A., Oren E., Chen K., Dai A.M., Hajaj N., Hardt M. et al. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 2018;1:18. DOI: 10.1038/s41746-018-0029-1.

7. Weng S.F., Reps J., Kai J., Garibaldi J.M., Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944. DOI: 10.1371/journal.pone.0174944.

8. Ma B., Yang J., Wong F.K.Y., Wong A.K.C., Ma T., Meng J. et al. Artificial intelligence in elderly healthcare: a scoping review. Ageing Res. Rev. 2023;83:101808. DOI: 10.1016/j.arr.2022.101808.

9. Ho A. Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatr. 2020;20(1):358. DOI: 10.1186/s12877-020-01764-9.

10. Velazquez-Diaz D., Arco J.E., Ortiz A., Pérez-Cabezas V., Lucena-Anton D., Moral-Munoz J.A. et al. Use of artificial intelligence in the identification and diagnosis of frailty syndrome in older adults: scoping review. J. Med. Internet Res. 2023;25:e47346. DOI: 10.2196/47346.

11. Moher D., Liberati A., Tetzlaff J., Altman D.G. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. DOI: 10.1371/journal.pmed.1000097.

12. Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. DOI: 10.1136/bmj.n71.

13. Alsaleh M.M., Allery F., Choi J.W., Hama T., McQuillin A., Wu H. et al. Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. Int. J. Med. Inform. 2023;175:105088. DOI: 10.1016/j.ijmedinf.2023.105088.

14. Kim H., Kim E., Lee I., Bae B., Park M., Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. Biotechnol. Bioprocess Eng. 2020;25(6):895–930. DOI: 10.1007/s12257-020-0049-y

15. Obuchi S.P., Kojima M., Suzuki H., Garbalosa J.C., Imamura K., Ihara K. et al. Artificial intelligence detection of cognitive impairment in older adults during walking. Alzheimers Dement. (Amst.). 2024;16(3):e70012. DOI: 10.1002/dad2.70012.

16. Wang Y., Ye Y., Shi S., Mao K., Zheng H., Chen X. et al. Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images. Aging Cell. 2024;23(8):e14196. DOI: 10.1111/acel.14196.

17. Wang Y.J., Yang K., Wen Y., Wang P., Hu Y., Lai Y. et al. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat. Med. 2024;30(5):1471–1480. DOI: 10.1038/s41591-024-02971-2.

18. Yang J., Liao M., Wang Y., Chen L., He L., Ji Y. et al. Opportunistic osteoporosis screening using chest CT with artificial intelligence. Osteoporos. Int. 2022;33(12):2547–2561. DOI: 10.1007/s00198-022-06491-y.

19. Chen H., Du H., Yi F., Wang T., Yang S., Pan Y. et al. Artificial intelligence-assisted oculo-gait measurements for cognitive impairment in cerebral small vessel disease. Alzheimers Dement. 2024;20(12):8516–8526. DOI: 10.1002/alz.14288.

20. Wang J., Liang Y., Cao S., Cai P., Fan Y. Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis. J. Med. Internet Res. 2023;25:e46014. DOI: 10.2196/46014.

21. Guo C., Pan J., Tian S., Gao Y. Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer. J. Int. Med. Res. 2023;51(11):3000605231198725. DOI: 10.1177/03000605231198725.

22. Song Y., Zhang D., Wang Q., Liu Y., Chen K., Sun J. et al. Prediction models for postoperative delirium in elderly patients with machine-learning algorithms and SHapley Additive exPlanations. Transl. Psychiatry. 2024;14(1):57. DOI: 10.1038/s41398-024-02762-w.

23. Loutati R., Ben-Yehuda A., Rosenberg S., Rottenberg Y. Multimodal machine learning for prediction of 30-day readmission risk in elderly population. Am. J. Med. 2024;137(7):617–628. DOI: 10.1016/j.amjmed.2024.04.002.

24. Ayers A.T., Ho C.N., Kerr D., Cichosz S.L., Mathioudakis N., Wang M. et al. Artificial intelligence to diagnose complications of diabetes. J. Diabetes Sci. Technol. 2025;19(1):246– 264. DOI: 10.1177/19322968241287773.

25. Sriram R.D., Reddy S.S.K. Artificial intelligence and digital tools: future of diabetes care. Clin. Geriatr. Med. 2020;36(3):513–525. DOI: 10.1016/j.cger.2020.04.009.

26. Yang J., Liao M., Wang Y., Chen L., He L., Ji Y. et al. Opportunistic osteoporosis screening using chest CT with artificial intelligence. Osteoporos. Int. 2022;33(12):2547–2561. DOI: 10.1007/s00198-022-06491-y.

27. Voltan G., Di Giovannantonio G., Carretta G., Vianello S., Contessa C., Veronese N. et al. A novel case-finding strategy based on artificial intelligence for the systematic identification and management of individuals with osteoporosis or at varying risk of fragility fracture. Arch. Osteoporos. 2024;19(1):45. DOI: 10.1007/s11657-024-01403-5.

28. Woodman R.J., Mangoni A.A. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clin. Exp. Res. 2023;35(11):2363–2397. DOI: 10.1007/s40520-023-02552-2.

29. Wang Y., Li N., Chen L., Wu M., Meng S., Dai Z. et al. Guidelines, Consensus Statements, and Standards for the Use of Artificial Intelligence in Medicine: Systematic Review. J. Med. Internet Res. 2023;25:e46089. DOI: 10.2196/46089.

30. Chu C.H., Nyrup R., Leslie K., Shi J., Bianchi A., Lyn A. et al. Digital ageism: challenges and opportunities in artificial intelligence for older adults. Gerontologist. 2022;62(7):947–955. DOI: 10.1093/geront/gnab167.

31. Aranda Rubio Y., Baztán Cortés J.J., Canillas Del Rey F. Is artificial intelligence ageist? Eur. Geriatr. Med. 2024;15(6):1957–1960. DOI: 10.1007/s41999-024-01070-2.

32. Burnazovic E., Yee A., Levy J., Gore G, Abbasgholizadeh Rahimi S. Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review. Arch. Gerontol. Geriatr. 2024;116:105129. DOI: 10.1016/j.archger.2023.105129.

33. Skuban-Eiseler T., Orzechowski M., Denkinger M., Kocar T.D., Leinert C., Steger F. Artificial intelligence-based clinical decision support systems in geriatrics: an ethical analysis. J. Am. Med. Dir. Assoc. 2023;24(9):1271–1276.e4. DOI: 10.1016/j.jamda.2023.06.008.

34. Alsaleh M.M., Allery F., Choi J.W., Hama T., McQuillin A., Wu H. et al. Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review. Int. J. Med. Inform. 2023;175:105088. DOI: 10.1016/j.ijmedinf.2023.105088.

35. Tang A., Ho R., Yu R., Huynh T., Luong S., Tam W. et al. Editorial: Can artificial intelligence help us overcome challenges in geriatrics? Geriatr. Nurs. 2023;52:A1–A2. DOI: 10.1016/j.gerinurse.2023.06.007.

36. Padhan S., Mohapatra A., Ramasamy S.K., Agrawal S. Artificial intelligence (AI) and robotics in elderly healthcare: enabling independence and quality of life. Cureus. 2023;15(8):e42905. DOI: 10.7759/cureus.42905.