Decoding the mind: A RAG-LLM on ICD-11 for decision support in psychology

Abstract

This paper explores the use of Large Language Models (LLMs) in mental health to assist psychologists and psychiatrists with diagnostic decision-making according to the ICD-11 classification system. ICD-11 is the 11th revision of the International Classification of Diseases, a globally used diagnostic tool for health conditions, including mental, behavioural, and neurodevelopmental disorders. In detail, we propose LLMind Chat, an AI-powered tool with a user-friendly interface designed to support mental health professionals in their diagnostic processes. LLMind Chat leverages a Retrieval Augmented Generation (RAG) model based on the Gemma 2 (27B parameters), specifically adapted to the context of the ICD-11. This RAG model combines the strengths of Gemma 2 with a comprehensive knowledge base derived from the ICD-11, allowing it to access and process relevant information from the classification manual in real-time. LLMind’s diagnostic accuracy was rigorously evaluated against the DSM-5-TR Clinical Cases manual, using automated metrics and mental health professionals’ expert validation. The result suggests that LLMind Chat can serve as a reliable decision-support tool, enhancing diagnostic reasoning and potentially reducing misclassifications. • LLMind Chat, an AI-powered tool for mental health diagnosis support. • Gemma 2 language model and ICD-11 for augmented retrieval generation. • LLMind’s diagnostic accuracy was evaluated against DSM-5-TR clinical cases. • LLMind Chat shows promise as a reliable support tool for clinicians.

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