RiskAgent: Autonomous Medical AI Copilot for Generalist Risk Prediction
This work addresses the gap between standardized AI evaluations and complex clinical decision-making for medical professionals, though it is incremental as it builds on existing LLM and clinical tool methods.
The paper tackles the problem of applying Large Language Models (LLMs) to real-world clinical risk prediction by developing RiskAgent, a system that collaborates with clinical decision tools to predict over 387 risk scenarios, achieving 76.33% accuracy on a new benchmark and outperforming commercial LLMs like GPT-4o by doubling its accuracy.
The application of Large Language Models (LLMs) to various clinical applications has attracted growing research attention. However, real-world clinical decision-making differs significantly from the standardized, exam-style scenarios commonly used in current efforts. In this paper, we present the RiskAgent system to perform a broad range of medical risk predictions, covering over 387 risk scenarios across diverse complex diseases, e.g., cardiovascular disease and cancer. RiskAgent is designed to collaborate with hundreds of clinical decision tools, i.e., risk calculators and scoring systems that are supported by evidence-based medicine. To evaluate our method, we have built the first benchmark MedRisk specialized for risk prediction, including 12,352 questions spanning 154 diseases, 86 symptoms, 50 specialties, and 24 organ systems. The results show that our RiskAgent, with 8 billion model parameters, achieves 76.33% accuracy, outperforming the most recent commercial LLMs, o1, o3-mini, and GPT-4.5, and doubling the 38.39% accuracy of GPT-4o. On rare diseases, e.g., Idiopathic Pulmonary Fibrosis (IPF), RiskAgent outperforms o1 and GPT-4.5 by 27.27% and 45.46% accuracy, respectively. Finally, we further conduct a generalization evaluation on an external evidence-based diagnosis benchmark and show that our RiskAgent achieves the best results. These encouraging results demonstrate the great potential of our solution for diverse diagnosis domains. To improve the adaptability of our model in different scenarios, we have built and open-sourced a family of models ranging from 1 billion to 70 billion parameters. Our code, data, and models are all available at https://github.com/AI-in-Health/RiskAgent.