Artificial Intelligence for Knee Osteoarthritis Care and Rehabilitation: A Systematic Review.

Journal: Pain management nursing : official journal of the American Society of Pain Management Nurses
Published Date: Aug 23, 2025

Abstract

OBJECTIVES: To explore how artificial intelligence (AI) can improve the clinical and rehabilitation management of knee osteoarthritis (KOA), emphasizing the unique contributions of specialized nurses. DESIGN: A systematic review was conducted to examine the integration of AI in the management of KOA, with a specific focus on implications for nursing practice. METHODS: This review followed established systematic review protocols. A comprehensive search of peer-reviewed qualitative and quantitative studies was conducted across PubMed, Google Scholar, and IEEE Xplore from January 1st, 2019, to May 1st, 2025. Studies were selected based on predefined inclusion and exclusion criteria. Data extraction and quality appraisal were independently performed by two reviewers using standardized tools. RESULTS: One key innovation is the use of AI-powered remote monitoring systems that collect data from wearable devices, allowing nurses to track patients' pain levels, joint mobility, and physical activity in real time. These systems enable continuous, remote assessment of symptoms, so nurses can intervene promptly if a patient's condition deteriorates. Additionally, AI-driven predictive analytics are helping nurses identify patients at higher risk for rapid disease progression or complications, allowing for early, proactive adjustments to care plans. Virtual health assistants and AI-based chatbots are also transforming patient education by answering common questions, guiding patients through home exercises, and providing reminders for medication and lifestyle adherence. By automating routine tasks such as documentation and appointment scheduling, AI reduces administrative burdens, giving nurses more time to focus on direct patient care. CONCLUSIONS: AI holds promise in revolutionizing KOA disease management by enabling nurses to deliver more effective, tailored care and ultimately improving patient outcomes.

Authors

  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
  • Ling Wang
    The State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, #7 Jinsui Road, Guangzhou, Guangdong 510230, China.
  • Lili Zhong
    Jilin Provincial Key Laboratory on Molecular and Chemical Genetic, The Second Hospital of Jilin University, Changchun, Jilin, 130041, People's Republic of China. [email protected].
  • Jianxin Feng
    Department of Interventional Therapy, People's Hospital of Baoji, Baoji City, 721000 Shaanxi Province, China.
  • Xue Wang
    Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening Biology Institute, Qilu University of Technology (Shandong Academy of Sciences) Jinan China.

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