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For each patient, individualized rehabilitation programs were created by three physiotherapists and by ChatGPT-4o and Gemini Advanced using structured prompts. The presence or absence of 50 clinically relevant rehabilitation parameters was recorded for each program. Chi-square tests were used to evaluate agreement rates between the LLMs and the physiotherapist-generated Consensus programs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>ChatGPT-4o achieved a 74% agreement rate with the physiotherapists\u2019 Consensus programs, while Gemini Advanced achieved 70%. Although both models showed high compatibility with general rehabilitation components, they demonstrated notable limitations in exercise specificity, including frequency, sets, and progression criteria. ChatGPT-4o performed as well as or better than Gemini in most phases, particularly in Phase 3, while Gemini showed lower consistency in balance and stabilization parameters.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>ChatGPT-4o and Gemini Advanced demonstrate promising potential in generating personalized rehabilitation programs for knee osteoarthritis. While their outputs generally align with expert recommendations, notable gaps remain in clinical reasoning and the provision of detailed exercise parameters. 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