ICF-Based Biopsychosocial Assessment With AI-Assisted Profile Prediction: Trapeziometacarpal Osteoarthritis Model

Trapeziometacarpal osteoarthritis (TMC OA) is a common condition affecting the base of the thumb that causes pain, weakness, and difficulty with daily hand use. Current clinical assessment often focuses on physical findings alone, without considering psychological and social factors that also influence patient outcomes. This study has three objectives organized as interrelated work packages: OBJECTIVE 1 (Clinical Assessment): To comprehensively assess individuals with TMC OA using the International Classification of Functioning, Disability and Health (ICF) framework. This includes evaluating pain, joint mobility, grip strength, daily activity limitations, social participation, psychological factors (anxiety, depression, fear of movement, pain beliefs), and environmental factors (family support, ergonomic adaptations). OBJECTIVE 2 (AI Knowledge Evaluation): To compare the performance of four large language models (GPT-5.2, Claude Opus 4.6, Gemini 2.5 Pro, LLaMA 4 Maverick) in answering patient questions about TMC OA, and to test whether adding a clinician-oriented system prompt changes that performance. Each question is submitted under two prompting conditions (zero-shot and clinician-oriented system prompt) and responses are rated by blinded experts for accuracy, comprehensiveness and clinical relevance, with readability assessed by automated indices. OBJECTIVE 3 (AI-Based Prediction): To analyze whether the best-performing large language model can predict multidimensional ICF-based patient profiles using only a limited set of core clinical parameters.