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

Journal: International journal of general medicine
Published Date: Jun 12, 2025

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.

Authors

  • Gang Feng
    Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Science, Haikou 571010, People's Republic of China; Key Laboratory of Monitoring and Control of Tropical Agricultural and Forest Invasive Alien Pests, Ministry of Agriculture, Haikou 571010, People's Republic of China. Electronic address: feng8513@sina.com.
  • Falin Weng
    The Department of Geriatrics at Wushan County People's Hospital, Chongqing Municipality, Chongqing, People's Republic of China.
  • Wei Lu
    Department of Pharmacy, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
  • Libin Xu
    Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States.
  • Wenxiang Zhu
    The Department of Geriatrics at Wushan County People's Hospital, Chongqing Municipality, Chongqing, People's Republic of China.
  • Man Tan
    The School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
  • Pengjuan Weng
    The Department of Geriatrics at Wushan County People's Hospital, Chongqing Municipality, Chongqing, People's Republic of China.

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