Artificial Intelligence in Older Adults’ Healthcare: Applications and Ethical Considerations

Abstract

With the global and national rise in the population of older adults, there has been a concurrent increase in chronic diseases, disease-related complications, care requirements, and the need for social support. The use of technology-assisted applications in the diagnosis, treatment, follow-up, and self-management processes of chronic diseases is becoming increasingly widespread. In older adults, artificial intelligence-based applications play a pivotal role across healthcare processes—spanning diagnosis, follow-up, treatment, adaptation, and rehabilitation—by addressing the physical and psychosocial changes associated with aging and chronic illness. These applications increase patient activity in self-management processes with many positive effects such as providing individualised care, detecting current and possible risks, facilitating the diagnosis process of diseases, supporting the process of treatment compliance, providing psychosocial support, strengthening the rehabilitation process. However, despite these benefits, the integration of artificial intelligence-based applications into older adults’ healthcare and their adaptation to these systems come with certain challenges and ethical concerns. Issues such as data privacy, algorithmic bias, digital literacy, and accessibility must be carefully considered to ensure the effective and equitable use of these technologies in the care of older adults. This review aims to comprehensively examine the role, efficacy, and potential of AI-driven applications in the management of chronic diseases among older adults, while also exploring the ethical and practical challenges that may arise. It is intended to guide healthcare professionals and researchers in developing ethically sensitive, person-centered, and accessible artificial intelligence-based strategies for optimizing the health and well-being of older adults.

Keywords

• Artificial Intelligence
• Ethics
• Health Care Delivery
• older adults

Yaşlı Sağlığında Yapay Zeka: Uygulamalar ve Etik Sorunlar

Abstract

Dünyada ve ülkemizde yaşlı nüfus artışı ile birlikte kronik hastalıklar, hastalıklarla ilişkili komplikasyonlar, bakım ihtiyacı ve sosyal destek ihtiyacı artış göstermektedir. Kronik hastalıkların tanı, tedavi, takip ve öz yönetim süreçlerinde teknoloji destekli uygulamaların kullanımını gittikçe yaygınlaşmaktadır. Yaşlılarda kronik hastalıklar ile birlikte yaşlılığa bağlı gelişen fiziksel ve psikososyal değişikliklerin tanı, takip, tedavi, uyum ve rehabilitasyon gibi sağlık bakım süreçlerinde yapay zeka destekli uygulamalar önemli bir rol oynamaktadır. Yapay zeka uygulamaları bireyselleştirilmiş bakım hizmeti sunma, mevcut ve olası riskleri saptama, hastalıkların tanılama sürecini kolaylaştırma, tedaviye uyum sürecine destek olma, psikososyal destek sağlama, rehabilitasyon sürecini güçlendirme gibi pek çok olumlu etkisi ile birlikte öz yönetim süreçlerinde hasta aktifliğini artırmaktadır. Ancak, tüm bu faydalarına rağmen, yapay zeka destekli uygulamaların yaşlı bireylerin sağlık hizmetlerine entegrasyonu ve bu sistemlere uyum sağlamaları sürecinde bazı zorluklar ve etik kaygılar da bulunmaktadır. Veri gizliliği, algoritmik önyargılar, dijital okuryazarlık ve bu teknolojilere erişilebilirlik gibi konuların ele alınması, yaşlı bakımında bu uygulamaların etkili ve adil bir şekilde kullanılmasını sağlamak açısından kritik önem taşımaktadır. Bu derleme, yaşlı bireylerin kronik hastalık yönetiminde yapay zeka destekli uygulamaların rolünü, etkinliğini, sunduğu olanakları ve karşılaşılabilecek etik ve uygulamaya yönelik zorlukları bütüncül bir şekilde ele almayı amaçlamıştır. Sağlık bakım profesyonellerine ve araştırmacılara, yaşlı bakımına yönelik yapay zeka temelli çözümleri değerlendirirken etik duyarlılığı yüksek, birey odaklı ve erişilebilir yaklaşımlar geliştirmeleri konusunda yol gösterici olabileceği düşünülmektedir.

Keywords

• Yapay Zeka
• Etik
• Sağlık Hizmeti Sunumu
• Yaşlı

References

1. Türkiye Cumhuriyeti Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı. On İkinci Kalkınma Planı (2024-2028). Türkiye Cumhuriyeti Cumhurbaşkanlığı Strateji ve Bütçe Başkanlığı; 2023 s. 241.
2. Türkiye İstatistik Kurumu. Erişim adresi: https:// data.tuik.gov.tr/Bulten/Index?p=Nufus-Projeksiyonlari-2023-2100-53699
3. Türkiye İstatistik Kurumu. 2024. Erişim adresi: https://data.tuik.gov.tr/Bulten/Index?p=Turkiye-Yasli-Profili-Arastirmasi-2023-53809
4. Abdi S, Spann A, Borilovic J, De Witte L, Hawley M. Understanding the care and support needs of older people: a scoping review and categorisation using the WHO international classification of functioning, disability and health framework (ICF). BMC Geriatr. 2019;19(1):1-15.
5. Çunkuş N, Yiğitoğlu GT, Akbaş E. Yaşlılık ve toplumsal dışlanma (Ageing and Social Exclusion). Journal of Geriatric Science. 2019;2(2):58- 67.
6. Akgerman A, Özdemir Yavuz ED, Kavaslar İ, Güngör S. Yapay zekâ ve hemşirelik (Artificial intelligence and nursing). JAIHS. 2022;2(1):21-7.
7. Kaplan M, Çakar F, Bingöl H. Sağlık alanında yapay zekanın kullanımı:derleme (The use of artificial intelligence in health: Review). MAUN¬SagBilDerg. 2024;4(3):75-85.
8. Jia X. Research on the emotional impact of AI care robots on elderly living alone. JAIP. 2023;6(6):50-5.

9. Yuan F, Miyazaki K, Ruiz-Navas S. An empirical analysis of aı related scientific knowledge and technologies to support elderly independent living. STIPM Journal. 2021;6(2):119-29.
10. Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020;22(5):1-8.
11. Wang Y, Xie C, Liang C, Zhou P, Lu L. Association of artificial intelligence use and the retention of elderly caregivers: A cross‐sectional study based on empowerment theory. J Nursing Management. 2022;30(8):3827-37.
12. Gayatri M. The well-being of elderly people during the COVID-19 pandemic: A narrative review. NHA. 2021;6(4):249-56.
13. Liu J. Design of Rural Home Smart Pension Monitoring System Based on Artificial Intelligence. In: Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023). Dordrecht: Atlantis Press International BV; 2023. s. 828- 35. (Atlantis Highlights in Engineering; c. 20). Erişim adresi: https://www.atlantis-press.com/ doi/10.2991/978-94-6463-262-0_86
14. Li L, Jiang L, Liu Z. Optimization research of artificial intelligence and wireless sensor networks in smart pension. Scientific Programming. 2021;2021:1-7.
15. Xu R, Cui Y. Research and Design of Health management system for the elderly. In: Rauf A, Zakuan N, Sohail MT, Azmi R, editörler. Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023. Dordrecht: Atlantis Press International BV; 2023. s. 26-35. (Atlantis Highlights in Engineering; c. 20). Erişim adresi: https://www.atlantis-press.com/ doi/10.2991/978-94-6463-262-0_4
16. Gündüz T, Eren F. Sağlıkta yapay zekâ: Bibliyometrik bir analiz (Artificial intelligence in health: A bibliometric analysis). SagAkaDerg. 2024;11(2):277-85.
17. Tuğaç Ç. Birleşmiş milletler sürdürülebilir kalkınma amaçlarının gerçekleştirilmesinde yapay zekâ uygulamalarının rolü (The role of artificial intelligence applications in the realization of the united nations sustainable development goals). Journal of Turkish Court of Accounts. 2023;34(128):73-99.
18. Zhao J, Lu Y, Zhu S, Li K, Jiang Q, Yang W. Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in ophthalmic disease diagnosis. Front Pharmacol. 2022;13:1-12.
19. Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inform.2019;7(3).
20. Nam KH, Seo I, Kim DH, Lee JI, Choi BK, Han IH. Machine learning model to predict osteoporotic spine with hounsfield units on lumbar computed tomography. J Korean Neurosurg Soc. 2019;62(4):442-9.
21. Singareddy S, Sn VP, Jaramillo AP, Yasir M, Iyer N, Hussein S, et al. Artificial intelligence and its role in the management of chronic medical conditions: a systematic review. Cureus. 2023;15(9):1-9.
22. Milella F, Russo DD, Bandini S. AI-powered solutions to support informal caregivers in their decision-making: a systematic review of the literature. OBM Geriatr. 2023;7(4):1-15.
23. Schachner T, Keller R, V Wangenheim F. Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. J Med Internet Res. 2020;22(9):1-16.
24. Tarumi S, Takeuchi W, Chalkidis G, Rodri¬guez-Loya S, Kuwata J, Flynn M, et al. Lever¬aging artificial intelligence to ımprove chronic disease care: methods and application to pharmacotherapy decision support for type-2 diabetes mellitus. Methods Inf Med. 2021;60(1):32- 43.
25. Alexander DS, Kiser S, North S, Roberts CA, Carpenter DM. Exploring community members’ perceptions to adopt a Tele-COPD program in rural counties. Exploratory Research in Clinical and Social Pharmacy. 2021;2:1-6.
26. Padhan S, Mohapatra A, Ramasamy SK, Agrawal S. Artificial intelligence (AI) and robotics in elderly healthcare: enabling independence and quality of life. Cureus. 2023;15(8):1-4.
27. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep. 2019;21(11):116.
28. Eun SJ, Kim EJ, Kim JY. Development and eval¬uation of an artificial intelligence–based cognitive exercise game: a pilot study. Journal of Environmental and Public Health. 2022;(1):1-15.
29. Inkster B, Sarda S, Subramanian V. An empathy-driven, conversational artificial intelligence agent (wysa) for digital mental well-being: real-world data evaluation mixed-methods study. JMIR Mhealth Uhealth. 2018;6(11):12106.
30. Saqib H, Sumyia, Alizeh Saqib. AI chatbots and psychotherapy: A match made in heaven? J Pak Med Assoc. 2023;73(11):2321-2321.
31. Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: A review. Sensors. 2021;21(6):1-29.
32. Yen JM, Lim JH. A clinical perspective on be¬spoke sensing mechanisms for remote monitoring and rehabilitation of neurological diseases: scoping review. Sensors. 2023;23(536):1-21.
33. Bai Y, Liu F, Zhang H. Artificial intelligence limb rehabilitation system on account of virtual reality technology on long-term health management of stroke patients in the context of the internet. Computational and Mathematical Methods in Medicine. 2022;2022:1-7.
34. Agrawal R, Pandey N. Developing rapport between humans and machines: emotionally ıntelligent AI assistants. IJRASET. 2024;12(3):1473- 80.
35. Hermann E. Anthropomorphized artificial intelligence, attachment, and consumer behavior. Mark Lett. 2022;33(1):157-62.
36. Santos NB, Bavaresco RS, Tavares JER, Ramos GDO, Barbosa JLV. A systematic mapping study of robotics in human care. Robotics and Autonomous Systems. 2021;144:1-24.
37. Musikanski L, Rakova B, Bradbury J, Phillips R, Manson M. Artificial intelligence and community well-being: A proposal for an emerging area of research. Int Journal of Com WB. 2020;3(1):39-55.
38. Marriott HR, Pitardi V. One is the loneliest number. Two can be as bad as one. The influence of AI Friendship Apps on users’ well‐being and addiction. Psychology and Marketing. 2024;41(1):86-101.
39. Chaibi A, Zaiem I. Doctor resistance of artificial ıntelligence in healthcare. International Journal of Healthcare Information Systems and Informatics. 2022;17(1):1-13.
40. Ejjami R. AI-Driven healthcare in France. IJFMR. 2024;6(3):1-27.
41. Alami H, Lehoux P, Denis JL, Motulsky A, Petitgand C, Savoldelli M, et al. Organizational readiness for artificial intelligence in health care: insights for decision-making and practice. JHOM. 2020;35(1):106-14.
42. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res. 2022;24(1):1-13.
43. Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):1-17.
44. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak. 2021;21(1):125.
45. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. IJERPH. 2021;18(1):1-18.
46. Tachkov K, Zemplenyi A, Kamusheva M, Dimitrova M, Siirtola P, Pontén J, et al. Barriers to use artificial intelligence methodologies in health technology assessment in central and east european countries. Front Public Health. 2022;10:1-9.
47. Laï MC, Brian M, Mamzer MF. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med. 2020;18(1):1-13.
48. Fan L. Artificial Intelligence Ethics: A Dialogue between technological advances and human values. IJEH. 2024;14(2):260-5.
49. World Health Organization. Ageism in Artificial Intelligence for Health: WHO Policy Brief. Geneva: World Health Organization; 2022 s. 1-12.
50. World Health Organization. Ethics and Governance of Artificial Intelligence for Health: Large Multi-Modal Models. WHO Guidance. Geneva: World Health Organization; 2024.