Artificial Intelligence in Chronic Disease Management for Aging Populations: A Systematic Review of Machine Learning and NLP Applications

Abstract

As China's elderly population grows rapidly and the aging society arrives, the number of elderly patients with chronic diseases (mainly including chronic cardiovascular and cerebrovascular diseases, respiratory diseases, etc) continues to increase, significantly impacting individuals, families, and society. Geriatric Chronic Disease Management in China faces multiple challenges, including unequal distribution of medical resources, lack of professional management teams, insufficient health education, improper medication management, inadequate psychological support, insufficient medical insurance coverage, and insufficient family support. The rise of artificial intelligence (AI) technology (eg, machine learning, deep learning, NLP, computer vision) offers possibilities for improving Geriatric Chronic Disease Management, including optimizing the distribution of medical resources, supplementing professional management teams, popularizing health education, optimizing medication management, enhancing psychological support, improving medical insurance efficiency and accuracy, and strengthening family support. However, the application of AI in Geriatric Chronic Disease Management still faces challenges such as the data scarcity, model generalization, clinician adoption, alignment of AI decision-making with clinical guidelines, Integration with existing healthcare systems, privacy and security, user acceptance, ethics and law. To overcome these challenges, interdisciplinary collaboration is needed to promote the rational and effective application of AI technology, aiming to achieve healthy aging. This paper systematically reviews the current status, challenges, and future directions of AI application in Geriatric Chronic Disease Management.

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