Clinical Decision Support Systems (CDSS) utilize evidence-based knowledge and patient data to offer real-time recommendations, with Large Language Models (LLMs) emerging as a promising tool to generate plain-text explanations for medical decisions. This study explores the effectiveness and reliability of LLMs in generating explanations for diagnoses based on patient complaints. Three experienced doctors evaluated LLM-generated explanations of the connection between patient complaints and doctor and model-assigned diagnoses across several stages. Experimental results demonstrated that LLM explanations significantly increased doctors' agreement rates with given diagnoses and highlighted potential errors in LLM outputs, ranging from 5% to 30%. The study underscores the potential and challenges of LLMs in healthcare and emphasizes the need for careful integration and evaluation to ensure patient safety and optimal clinical utility.
@article{arxiv.2310.01708,
title = {Deciphering Diagnoses: How Large Language Models Explanations Influence Clinical Decision Making},
author = {D. Umerenkov and G. Zubkova and A. Nesterov},
journal= {arXiv preprint arXiv:2310.01708},
year = {2023}
}