Multi-Agent Clinical Reasoning Framework

# Multi-Agent Clinical Reasoning System The Multi-Agent Clinical Reasoning System is a lightweight collaborative AI framework designed to evaluate how iterative multi-agent reasoning workflows can improve clinical decision-making, diagnostic consistency, and reasoning transparency in healthcare-focused AI systems. Rather than relying on a single language model response, the project introduces a structured reasoning pipeline where multiple specialized AI agents collaborate to analyze, critique, and refine medical reasoning outputs. The system is built around three coordinated agents: * **Solver Agent** : Generates an initial clinical interpretation and diagnostic reasoning pathway. * **Critic Agent** : Reviews the generated reasoning to identify logical inconsistencies, weak assumptions, or missing evidence. * **Refiner Agent** : Produces an improved and more reliable final response by incorporating critique feedback into the reasoning process. This workflow simulates iterative expert review commonly used in real-world medical decision-making environments, where multiple specialists contribute to refining diagnostic conclusions. The project uses the MedQA benchmark dataset to evaluate reasoning performance on healthcare-related question-answering tasks. Lightweight open-source language models are used to ensure efficient inference and accessibility in cloud-based environments such as Google Colab. ### Technologies Used * Python * CrewAI * Transformers * PyTorch * Gradio * Qwen2.5-1.5B-Instruct * TinyLlama-1.1B-Chat-v1.0 * Google Colab * Hugging Face Datasets The system architecture is modular and extensible, allowing additional reasoning agents, evaluation layers, or domain-specific healthcare workflows to be integrated in future iterations. The project demonstrates how coordinated multi-agent AI systems can support more transparent, reliable, and scalable reasoning pipelines for healthcare AI research and intelligent decision-support applications.