Decoding the mind: A RAG-LLM on ICD-11 for decision support in psychology
Marco Cremaschi, Davide Ditolve, Cesare Curcio, Anna Panzeri, Andrea Spoto, Andrea Maurino
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.