Clinician-Supervised Large Language Models Generated Exercise Protocols for Degenerative Knee Disease: A Four-Arm Pilot Randomized Trial

Large language models (LLMs) can generate standardized, evidence-informed rehabilitation protocols, yet their effect on patient outcomes remains uncertain. We conducted a single-centre, four-arm randomized pilot trial in which 52 adults with degenerative knee disease were allocated equally to conventional physiotherapy alone (Control; n = 13) or to conventional physiotherapy plus an LLM-generated exercise protocol (ChatGPT-5, Gemini 2.5 Pro, or DeepSeek V3.1; n = 13 each). Before recruitment, a single standardized prompt was issued to each LLM to generate an 8-week supervised progressive exercise protocol for a representative patient scenario using standard clinic equipment. After safety review, each protocol was applied unchanged to all participants in its corresponding group alongside identical co-interventions, delivered twice weekly for 8 weeks. The primary outcome was change in Knee Injury and Osteoarthritis Outcome Score (KOOS) total score (0–100) from baseline to week 8. KOOS improved in all groups, with mean changes of +6.2 ± 14.5 in Control, +14.0 ± 8.9 in ChatGPT-5, +16.6 ± 10.0 in Gemini, and +16.2 ± 14.6 in DeepSeek; between-group differences were not statistically significant (p ≈ 0.12). No serious adverse events occurred. In this pilot trial, LLMs functioned as protocol generators under clinician supervision, producing feasible and safe exercise programs with numerically larger KOOS gains than clinician-designed usual care. Larger trials are needed to confirm comparative effectiveness.