Large Language Models for Depression Assessment: Simulating Patients and Clinicians in MADRS Administration

Depression assessment faces challenges of resource limitations and inter-rater variability. We evaluated four large language models’ abilities to simulate depressed patients and conduct Montgomery-Asberg Depression Rating Scale (MADRS) assessments using 139 synthetic patient profiles. Four configurations were tested: bidirectional role-playing between different LLMs (Grok 4 and Claude 4.1), dual-role assessments using single LLM instances (GPT5 and Gemini 2.5), and variations in patient response patterns. Results demonstrated high concordance between true and LLM-estimated MADRS scores (ICCs=0.79-0.93, p