Artificial intelligence in geriatric medicine: potential and challenges. Systematic review
https://doi.org/10.37586/2686-8636-4-2025-557-570
Abstract
BACKGROUND. The growing global aging population increases the demand for innovative solutions in geriatric medicine to address complex health challenges. AI offers promising tools for enhancing care, but faces unique challenges in this area.
OBJECTIVE. To evaluate the potential of AI to enhance diagnosis, monitoring and care for elderly patients in geriatric medicine and to identify key challenges to its implementation.
MATERIALS AND METHODS. A systematic review was conducted according to PRISMA guidelines. Literature searches were conducted in PubMed, Scopus and RSCI databases (2020-2025), using keywords related to AI and geriatrics. Studies focused on clinical applications of AI in patients aged 60 years and over were included. After removing duplicates and irrelevant publications from 1,243 records, 50 studies were analyzed. The quality of the studies was assessed using the AMSTAR-2 and the Newcastle-Ottawa scales.
RESULTS. AI demonstrates high efficacy in early diagnosis of dementia (up to 90 % accuracy), osteoporosis (89 %), and cardiovascular diseases (91 %), as well as in monitoring falls (92 %) and nutritional status (90 %). Key challenges include ethical concerns (privacy, algorithmic bias), limited technology access (40 % in rural areas), and insufficient staff training (only 30 % of geriatricians are AI-proficient).
CONCLUSIONS. AI holds transformative potential for geriatric medicine but requires adaptation to the unique needs of older adults, development of ethical and technical standards, and enhanced training programs for healthcare professionals. This review underscores the need to integrate AI as part of a person-centered care ecosystem.
About the Author
A. V. MartynenkoУзбекистан
Martynenko Alexandr Vladimirovich
Tashkent
References
1. World Health Organization. Ageing and health. [Электронный ресурс] / WHO. 2022. Режим доступа: https://www.who.int/news-room/fact-sheets/detail/ageing-andhealth, свободный.
2. The Demographic Yearbook of Russia. 2023: Statistical Handbook (In Russ.). Режим доступа: https://rosstat.gov.ru/folder/210/document/13207, свободный.
3. Kemoun P., Ader I., Planat-Benard V., et al. A gerophysiology perspective on healthy ageing. Ageing Res Rev. 2022 ; 73 : 101537. doi: 10.1016/j.arr.2021.101537.
4. Majumder A., Sen D. Artificial intelligence in cancer diagnostics and therapy: current perspectives. Indian J Cancer. 2021 ; 58 (4) : 481–492. doi: 10.4103/ijc.IJC_399_20.
5. Itchhaporia D. Artificial intelligence in cardiology. Trends Cardiovasc Med. 2022 ; 32 (1) : 34–41. doi: 10.1016/j.tcm.2020.11.007.
6. Wang H., Fu T., Du Y., et al. Scientific discovery in the age of artificial intelligence. Nature. 2023 ; 620 (7972) : 47–60. doi: 10.1038/s41586-023-06221-2.
7. Lyu Y. X., Fu Q., Wilczok D., et al. Longevity biotechnology: bridging AI, biomarkers, geroscience and clinical applications for healthy longevity. Aging (Albany NY). 2024 ; 16 (20) : 12955–12976. doi: 10.18632/aging.206135.
8. Yu K. H., Healey E., Leong T. Y., et al. Medical Artificial Intelligence and Human Values. N Engl J Med. 2024 ; 390 (20) : 1895–1904. doi: 10.1056/NEJMra2214183.
9. Keskinbora K. H. Medical ethics considerations on artificial intelligence. J Clin Neurosci. 2019 ; 64 : 277–282. doi: 10.1016/j.jocn.2019.03.001.
10. Page M. J., McKenzie J. E., Bossuyt P. M., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021 ; 372 : n71. doi: 10.1136/bmj.n71.
11. Umeda-Kameyama Y., Kameyama M., Tanaka T., et al. Screening of Alzheimer’s disease by facial complexion using artificial intelligence. Aging (Albany NY). 2021 ; 13 (2) : 1765–1772. doi: 10.18632/aging.202545.
12. Wang Y., Ye Y., Shi S., 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.
13. Yang J., Liao M., Wang 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.
14. Voltan G., Di Giovannantonio G., Carretta G., 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.
15. Gao J. M., Ren Z. H., Pan X., et al. Identifying peripheral arterial disease in the elderly patients using machine-learning algorithms. Aging Clin Exp Res. 2022 ; 34 (3) : 679–685. doi: 10.1007/s40520-021-01985-x.
16. Chen H., Du H., Yi F., et al. Artificial intelligenceassisted oculo-gait measurements for cognitive impairment in cerebral small vessel disease. Alzheimers Dement. 2024 ; 20 (12) : 8516–8526. doi: 10.1002/alz.14288.
17. Maleki S. F., Yousefi M., Sobhi N., et al. Artificial Intelligence in Eye Movements Analysis for Alzheimer’s Disease Early Diagnosis. Curr Alzheimer Res. 2024 ; 21 (3) : 155–165. doi: 10.2174/0115672050322607240529075641.
18. Prada A. G., Stroie T., Diculescu R. I., et al. Artificial Intelligence as a Tool in Diagnosing Inflammatory Bowel Disease in Older Adults. J Clin Med. 2025 ; 14 (4) : 1360. doi: 10.3390/jcm14041360.
19. Obuchi S. P., Kojima M., Suzuki H., 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.
20. Yenişehir S. Artificial intelligence based on falling in older people: A bibliometric analysis. Aging Med (Milton). 2024 ; 7 (2) : 162–170. doi: 10.1002/agm2.12302.
21. Wang Y., Bai L., Yang J., et al. Artificial intelligence measuring the aortic diameter assist in identifying adverse blood pressure status including masked hypertension. Postgrad Med. 2022 ; 134 (1) : 111–121. doi: 10.1080/00325481.2021.2003150.
22. Papathanail I., Brühlmann J., Vasiloglou M. F., et al. Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients. Nutrients. 2021 ; 13 (12) : 4539. doi: 10.3390/nu13124539.
23. Yang F., Chen H., Shan Y., et al. Preventing postoperative moderate-and high-risk pressure injuries with artificial intelligence-powered smart decompression mattress on in middle-aged and elderly patients. Br J Hosp Med (Lond). 2024 ; 85 (8) : 1–13. doi: 10.12968/hmed.2024.0112.
24. Chien S. C., Yen C. M., Chang Y. H., et al. Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in LongTerm Care for Older People. J Med Internet Res. 2025 ; 27 : e56692. doi: 10.2196/56692.
25. Wilson P. M., Ramar P., Philpot L. M., et al. Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial. J Pain Symptom Manage. 2023 ; 66 (1) : 24–32. doi: 10.1016/j.jpainsymman.2023.02.317
26. Murawski A., Ramirez-Zohfeld V., Mell J., et al. NegotiAge: Development and pilot testing of an artificial intelligence-based family caregiver negotiation program. J Am Geriatr Soc. 2024 ; 72 (4) : 1112–1121. doi: 10.1111/jgs.18775.
27. 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.
28. Tanaka K., Okazaki H., Omura T., et al. Enhancing Diabetes Management for Older Patients: The Potential Role of ChatGPT. Geriatr Gerontol Int. 2024 ; 24 (8) : 816–817. doi: 10.1111/ggi.14933.
29. Sun M. Y., Wang Y., Zheng T., et al. Health economic evaluation of an artificial intelligence (AI)-based rapid nutritional diagnostic system for hospitalised patients. Clin Nutr. 2024 ; 43 (10) : 2327–2335. doi: 10.1016/j.clnu.2024.08.030.
30. Rosen T., Zhang Y., Bao Y., et al. Can artificial intelligence help identify elder abuse and neglect? J Elder Abuse Negl. 2020 ; 32 (1) : 97–103. doi: 10.1080/08946566.2019.1682099.
31. Piscitello G. M., Rogal S., Schell J., et al. Equity in Using Artificial Intelligence Mortality Predictions to Target Goals of Care Documentation. J Gen Intern Med. 2024 ; 39 (15) : 3001–3008. doi: 10.1007/s11606-024-08849-w.
32. 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.
33. 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.
34. Brender T. D., Smith A. K., Block B. L. Can Artificial Intelligence Speak for Incapacitated Patients at the End of Life? JAMA Intern Med. 2024 ; 184 (9) : 1005–1006. doi: 10.1001/jamainternmed.2024.2676.
35. Chu C. H., Nyrup R., Leslie K., et al. Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults. Gerontologist. 2022 ; 62 (7) : 947–955. doi: 10.1093/geront/gnab167.
36. 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.
37. Choudhury A., Renjilian E., Asan O. Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review. JAMIA Open. 2020 ; 3 (3) : 459–471. doi: 10.1093/jamiaopen/ooaa034.
38. Gallistl V., Banday M. U .L., Berridge C., et al. Addressing the Black Box of AI — A Model and Research Agenda on the Coconstitution of Aging and Artificial Intelligence. Gerontologist. 2024 ; 64 (6) : gnae039. doi: 10.1093/geront/gnae039.
39. Burnazovic E., Yee A., Levy J., et al. 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.
40. Zhang L., Li J. Prospects for the application of artificial intelligence in geriatrics. J Transl Int Med. 2025 ; 12 (6) : 531–533. doi: 10.1515/jtim-2024-0034.
41. Stefanacci R. G. Artificial intelligence in geriatric medicine: Potential and pitfalls. J Am Geriatr Soc. 2023 ; 71 (11) : 3651–3652. doi: 10.1111/jgs.18569.
42. Haque N. Artificial intelligence and geriatric medicine: New possibilities and consequences. J Am Geriatr Soc. 2023 ; 71 (6) : 2028–2031. doi: 10.1111/jgs.18334.
43. Rosselló-Jiménez D., Docampo S., Collado Y., et al. Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey. Eur Geriatr Med. 2024 ; 15 (4) : 1129–1136. doi: 10.1007/s41999-024-00970-7.
44. 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.
45. Bednorz A., Mak J. K. L., Jylhävä J., Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging. 2023 ; 18 : 2171–2183. doi: 10.2147/CIA.S400887.
46. Kameyama M., Umeda-Kameyama Y. Applications of artificial intelligence in dementia. Geriatr Gerontol Int. 2024 ; 24 (Suppl 1) : 25–30. doi: 10.1111/ggi.14709.
47. Ho S. Y., Chien T. W., Lin M. L., Tsai K. T. An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN). Medicine (Baltimore). 2023 ; 102 (4) : e32670. doi: 10.1097/MD.0000000000032670.
48. Braithwaite D., Karanth S. D., Divaker J., et al. Evaluating ChatGPT’s accuracy in providing screening mammography recommendations among older women. J Am Geriatr Soc. 2024 ; 72 (7) : 2237–2240. doi: 10.1111/jgs.18854.
49. Abadir P., Oh E., Chellappa R., et al. Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer’s care. Alzheimers Dement. 2024 ; 20 (4) : 3074–3079. doi: 10.1002/alz.13710.
50. Long V. Is artificial intelligence useful in the practice of geriatric dermatology? Clin Dermatol. 2024 ; 42 (5) : 443–446. doi: 10.1016/j.clindermatol.2024.06.012.
51. Cho H., Oh O., Greene N., et al. Engagement of Older Adults in the Design, Implementation and Evaluation of Artificial Intelligence Systems for Aging: A Scoping Review. J Gerontol A Biol Sci Med Sci. 2025 : glaf024. doi: 10.1093/gerona/glaf024.
52. White A., Maguire M. B., Brown A., Keen D. Impact of Artificial Intelligence on Nursing Students’ Attitudes toward Older Adults. Nurs Rep. 2024 ; 14 (2) : 1129–1135. doi: 10.3390/nursrep14020085.
53. Rodríguez-Sánchez I., Pérez-Rodríguez P. La revolución gerontotecnológica: integrando la inteligencia artificial para mejorar la vida de las personas mayores. Rev Esp Geriatr Gerontol. 2024 ; 59 (1) : 101409. doi: 10.1016/j.regg.2023.101409.
54. Chen L. K. Gerontechnology and artificial intelligence: Better care for older people. Arch Gerontol Geriatr. 2020 ; 91 : 104252. doi: 10.1016/j.archger.2020.104252.
55. Wang J., Liang Y., Cao S., et al. Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis. J Med Internet Res. 2023 ; 25 : e46014. doi: 10.2196/46014.
56. Tang A., Ho R., Yu R., 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.
57. Karim H. T., Vahia I. V., Iaboni A., Lee E. E. Editorial: Artificial Intelligence in Geriatric Mental Health Research and Clinical Care. Front Psychiatry. 2022 ; 13 : 859175. doi: 10.3389/fpsyt.2022.859175.
58. 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. Erratum in: J Gerontol A Biol Sci Med Sci. 2024 ;79 (5) : glae111. doi: 10.1093/gerona/glae111.
59. Haque N. Reply to: Artificial intelligence in geriatric medicine: Potentials and pitfalls. J Am Geriatr Soc. 2023 ; 71 (11) : 3652–3653. doi: 10.1111/jgs.18567.
60. Fontecha-Gómez B. J., Betancor-Santana É. Inteligencia artificial en geriatría. Impacto de ChatGPT e IA. Rev Esp Geriatr Gerontol. 2023 ; 58 (6) : 101403. doi: 10.1016/j.regg.2023.101403.
Supplementary files
Review
For citations:
Martynenko A.V. Artificial intelligence in geriatric medicine: potential and challenges. Systematic review. Russian Journal of Geriatric Medicine. 2025;(4):557-570. (In Russ.) https://doi.org/10.37586/2686-8636-4-2025-557-570
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