Introduction
With the growth in the aged population and the increase in the health needs of older people, new challenges have been raised for the health care systems. The main goal of managing the health needs of older adults is to maintain their independence and increase their sense of well-being and life satisfaction. In this context, artificial intelligence (AI) as an advanced technology, has a huge capacity to manage the health services, prevent chronic diseases, and increase the quality of life in older adults. It can be used in various aspects of geriatric care management, including remote monitoring, fall detection, medication management, and cognitive aids. The AI-based programs not only can improve the quality of care provided to older adults, but also can enable them to maintain their independence. Considering the limitations of existing researches in this field, the present research aims to investigate the applications of AI in geriatric care management.

Methods & Materials 
The is meta-synthesis review study on articles related to the applications of AI in geriatric care management to collect data, which were searched in databases such as Emerald, Web of Science, Springer, Scopus, NoorMegs, MagIran, Civilica, with a time limitation from 2000 to 2023 using the keywords “health management”, “smart health systems”, “automation of health systems”, “older adults health management”, “health management and artificial intelligence”, “older adults health and artificial intelligence”, “automation and older adults health” with OR operator. The initial search yielded 624 articles for meta-synthesis. To select eligible articles, their titles, abstracts, content, and quality were evaluated. Finally, 25 articles were selected and analyzed. MAXQDA software, version 2020 was used to code the concepts. Figure 1 shows the diagram of the article selection process.

Results
The Kappa index value was 0.75 which confirmed the reliability of the factors identified in this research. From 122 initial codes, We identified 8 main themes, 21 sub-themes or concepts, and 42 indicators. The identified themes were: Prediction and prevention of diseases (with three concepts: Prediction algorithms based on health history, intelligent health warning systems, and physical activity/healthy lifestyle incentive programs), management of chronic diseases (with three concepts: Personalized treatment apps, treatment matching algorithms, and real-time health monitoring systems), organizing and sorting medications (with three concepts: Intelligent medication management systems, scheduled medication reminder apps, and pharmacy connection systems), smart home monitoring (with three concepts: Intelligent early warning systems in emergencies, behavior/activity monitoring apps, and the apps for communication with rescue teams in emergencies), online communicatiion with doctors (with two concepts: Telemedicine and online consultation systems, and security algorithms to protect medical information), car assistance systems (with three concepts: Assistive technologies for safe driving, driver health monitoring systems, and car sensors to identify the physical problems of driver), management of health services (with two concepts: Algorithms for prioritizng health services and smart apps for scheduling medical appointments), psychological support (with two concepts: Counseling/psychological support systems and psychological problem detection/managment algorithms).
By using AI-based algorithms and systems, it is possible to help older adults in prediction and prevention of diseases such as diabetes and hypertension, by suggesting appropriate treatments and continuous monitoring. By using AL-based systems, it is possible to help older adults with organizing and sorting medications and preventing the risks of drug side effects. With the use of sensors and smart apps, AI can play an important role in monitoring older adults at home and reporting unusual situations. AI can enable effective online communication between older adults and doctors. AI-based assistance sensors in cars can help maintain the safety and comfort of older adults and prevent car accidents. AI can be useful in improving the provision of health services to older adults by optimizing processes and increasing the quality of services. By using AI in mental support, it is possible to help provide psychological support and counseling services to them and maintain or improve their mental health.

Conclusion
According to the findings, it can be said that AI has significant impacts on geriatric care management and can help improve the quality of life of older adults. AI-based systems can use machine learning algorithms to analyze their medical data and help in the early diagnosis of diseases or predicting the health status. The AI can be employed in determining risk factors for chronic diseases in older adults, designing prevention programs specific to each person, and optimal management of chronic diseases. Officials and relevant organizations in Iran should pay more attention to the health of older adults by using AI-based technologies in managing their health needs.

Ethical Considerations
Compliance with ethical guidelines
In this study, all ethics were considered. Since this is a review study, the need for ethical code was waived.

Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

Authors' contributions
All authors contributed equally to the conception and design of the study, data collection and analysis, interpretation of the results, and drafting of the manuscript. Each author approved the final version of the manuscript for submission.

Conflicts of interest
The authors declared no conflict of interest.

References

1. Hamedanchi A, Nafei A, Hamzezadeh H, Lakpour M, Rezaie F. [Artificial intelligence and ageing (Persian)]. Journal of Gerontology. 20233; 8 (2):80-5.‎ [DOI:10.22034/JOGE.8.2.80]
2. Ma B, Yang J, Wong FKY, Wong AKC, Ma T, Meng J, et al. Artificial intelligence in elderly healthcare: A scoping review. Ageing Research Reviews. 2023; 83:101808. [DOI:10.1016/j.arr.2022.101808] [PMID]
3. Ho A. Are we ready for artificial intelligence health monitoring in elder care? BMC Geriatrics. 2020; 20(1):358. [DOI:10.1186/s12877-020-01764-9] [PMID]
4. Yan L. Application method of environmental protection building elements based on artificial intelligence technology in the field of urban planning and design. Advances in Multimedia. 2022; 4:1-11. [DOI:10.1155/2022/8994088]
5. Lee CH, Wang C, Fan X, Li F, Chen CH. Artificial intelligence-enabled digital transformation in elderly healthcare field: Scoping review. Advanced Engineering Informatics. 2023; 55:101874. [DOI:10.1016/j.aei.2023.101874]
6. Porkodi S, Kesavaraja D. Healthcare robots enabled with IoT and artificial intelligence for elderly patients. In: Porkodi S, Kesavaraja D, Dubey AK, Kumar A, Kumar SR, Gayathri N, Das P, editors. AI and IoT-Based Intelligent Automation in Robotics. Texas: Scrivener Publishing LLC; 2021. [Link]
7. Yoshany N, Seyed Khameshi SS, Rezaei M, Baqian N, Karimian Kakolaki Z. [Relationship between quality of life and using smart phones in the elderly (Persian)]. J Educ Community Health. 2019; 6(4):247-55. [DOI:10.29252/jech.6.4.247]
8. Safdari R, Shams Abadi A R, Pahlevany Nejad S. [Improve health of the elderly people with m-health and technology (Persian)]. Salmand: Iranian Journal of Ageing. 2018; 13(3):288-99.‎ [DOI:10.32598/sija.13.3.288]
9. Li X. Evaluation and analysis of elderly mental health based on artificial intelligence. Occupational Therapy International. 2023; 2023:7077568. [DOI:10.1155/2023/7077568] [PMID]
10. Awotunde JB, Ajagbe SA, Florez H. Internet of Things with Wearable Devices and Artificial Intelligence for Elderly Uninterrupted Healthcare Monitoring Systems. In: Florez H, Gomez H, editors. Applied Informatics. ICAI 2022. Communications in Computer and Information Science, 1643. Cham: Springer; 2022. [DOI:10.1007/978-3-031-19647-8_20]
11. Ganesh D, Seshadri G, Sokkanarayanan S, Rajan S, Sathiyanarayanan M. Iot-based google duplex artificial intelligence solution for elderly care. Paper presented at: 2019 International Conference on contemporary Computing and Informatics (IC3I). 12-14 December 2019; Singapore. [DOI:10.1109/IC3I46837.2019.9055551]
12. Abedi S, Hamidi F, Sanayi MR. [Design a fuzzy artificial intelligence system in the selection of health technology in the futuristic process (Persian)]. Journal of Management Future Research. 32(128):28-40. [Link]
13. Falamarzi F, Shaker H, Pourzad M. [Using new medical technology in the process of improving the life of the elderly(Persian)]. Paper presented at: 5th congress of Student Research Committee of Rafsanjan University of Medical Sciences. 4 December 2019;  Rafsanjan, Iran. [Link]

14. Dilip G, Guttula R, Rajeyyagari S, Pandey RR, Bora A, R Kshirsagar P, et al. Artificial intelligence-based smart comrade robot for elders healthcare with strait rescue system. Journal of Healthcare Engineering. 2022; 2022:9904870. [DOI:10.1155/2022/9904870] [PMID]
15. Ihde D. Technology and the lifeworld: From garden to earth. Indiana: Indiana University Press; 1990. [Link]
16. Wang, L. L., Jia, L. Q., Chu, F. Q., & Li, M. X. (2021). Design of home care system for rural elderly based on artificial intelligence. Paper presented at: International Conference on Computer Big Data and Artificial Intelligence (ICCBDAI 2020). 24-25 October 2020, Changsha, China. [DOI:10.1088/1742-6596/1757/1/012057]
17. Zhavoronkov A, Mamoshina P, Vanhaelen Q, Scheibye-Knudsen M, Moskalev A, Aliper A. Artificial intelligence for aging and longevity research: Recent advances and perspectives. Ageing Research Reviews. 2019; 49:49-66. [DOI:10.1016/j.arr.2018.11.003] [PMID]
18. Fritz RL, Dermody G. A nurse-driven method for developing artificial intelligence in "smart" homes for aging-in-place. Nursing Outlook. 2019; 67(2):140-53. [DOI:10.1016/j.outlook.2018.11.004][PMID]
19. Chowdhury M, Cervantes EG, Chan WY, Seitz DP. Use of machine learning and artificial intelligence methods in geriatric mental health research involving electronic health record or administrative claims data: A systematic review. Frontiers in Psychiatry. 2021; 12:738466. [DOI:10.3389/fpsyt.2021.738466] [PMID]
20. Rubeis G. The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care.Archives of Gerontology and Geriatrics. 202; 91:104186. [DOI:10.1016/j.archger.2020.104186] [PMID]
21. Graham SA, Lee EE, Jeste DV, Van Patten R, Twamley EW, Nebeker C, et al. Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review. Psychiatry Research. 2020; 284:112732. [DOI:10.1016/j.psychres.2019.112732]
22. Sapci, A. H., & Sapci, H. A. (2019). Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: Systematic review. JMIR Aging, 2(2), e15429. [DOI:10.2196/15429] [PMID]
23. Sumit K, Prapti Sh, Megha K. AI -Based health management system for elderly people. Paper presented at: 2024 2nd International Conference on Disruptive Technologies (ICDT).  15-16 March 2024; Greater Noida, India. [DOI:10.1109/ICDT61202.2024.10488983]
24. Ahmed MU, Barua S, Begum S. Artificial Intelligence, Machine Learning and Reasoning in Health Informatics-Case Studies. In: Ahad MAR, Ahmed MU, editors. Signal Processing Techniques for Computational Health Informatics. Intelligent Systems Reference Library, vol 192. Cham: Springer; 2021. [Link]
25. Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020; 33(1):10-18. [DOI:10.1177/0840470419873123] [PMID]
26. Wu C, Xu H, Bai D, Chen X, Gao J, Jiang X. Public perceptions on the application of artificial intelligence in healthcare: A qualitative meta-synthesis. BMJ Open. 2023; 13(1):e066322.   [DOI:10.1136/bmjopen-2022-066322] [PMID]

27. Qian K, Zhang Z, Yamamoto Y, Schuller BW. Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring. IEEE Signal Processing Magazine. 2021;  38(4). [DOI:10.1109/MSP.2021.3057298]
28. Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of artificial intelligence in medicine: An overview. Current Medical Science. 2021; 41(6):1105-15. [DOI:10.1007/s11596-021-2474-3] [PMID]
29. Briganti G, Le Moine O. Artificial intelligence in medicine: Today and tomorrow. Frontiers in Medicine. 2020; 7:27. [DOI:10.3389/fmed.2020.00027] [PMID]
30. Kazemi E, Mehrabi N, hajipurtalebi A. [An overview of the artificial intelligence applications in identifying and combating the Covid-19 Pandemic (Persian)]. Paramedical Sciences and Military Health. 2021; 16(3):52-65. [Link]
31. Saduqi F, Sheikh Taheri A, Mohseni H. [Application of Artificial Intelligence Systems in Medical Decisions: Benefits and Challenges (Persian)]. Health Information Management. 2018; 8(3):23-39. [Link]
32. Sandelowski M, Barroso J. Handbook for synthesizing qualitative research. New York: Springer Publishing Company; 2007. [Link]
33. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006; 3(2):77-101. [Link]
34. Shiwani T, Relton S, Evans R, Kale A, Heaven A, Clegg A, et al. New Horizons in artificial intelligence in the healthcare of older people. Age and Ageing. 2023; 52(12):afad219. d [DOI:10.1093/ageing/afad219] [PMID]