Research protocol: Evaluating Chain-of-Thought Reasoning in LLMs for Complex Clinical Psychiatric Cases
Background Psychiatric medicine presents unique diagnostic and therapeutic challenges, often involving multimorbidity, polypharmacy, and atypical presentations requiring complex reasoning. Artificial Intelligence, particularly Large Language Models (LLMs), is emerging as a support tool in these settings. However, the clinical validity, interpretability, and reliability of LLMs in psychiatry remain largely unexplored, particularly their ability to generate transparent, guideline-consistent reasoning. Aims This study evaluates the clinical reasoning capabilities of LLMs in complex psychiatric scenarios. The primary aim is to assess the validity of their long chain-of-thought (CoT) reasoning. A secondary aim is to determine whether LLM assistance improves clinicians’ diagnostic accuracy. Method Three LLMs, Gemini 2.5, Grok, and DeepSeek R1, will be assessed using ten complex psychiatric cases sourced from a non-public clinical manual and rated with the Amsterdam Clinical Challenge Scale. Each model's CoT response will be evaluated by blinded panels of psychiatrists, residents, and general practitioners using standardized metrics for factual accuracy, coherence, and medical plausibility. In a second phase, clinicians will answer thirty diagnostic questions with and without support from the best-performing LLM. The study uses step-by-step reasoning prompts and few-shot examples to elicit detailed responses and includes bias mitigation strategies such as randomization, blinding, and statistical controls. Results Analyses will assess inter-rater reliability, metric redundancy, and reasoning quality. Closed-source models are expected to outperform open-source ones. LLM assistance is anticipated to improve diagnostic accuracy, especially among non-specialists. Conclusions This study provides a framework for evaluating LLMs in psychiatry and supporting their safe, evidence-based integration into mental health practice.