用真实医学工具增强大模型，让AI更准地预测疾病风险。
RiskAgent: Synergizing Language Models with Validated Tools for Evidence-Based Risk Prediction
- 将大模型与数百个循证医学工具结合，避免幻觉。
- 在多种疾病风险预测中表现优于现有方法。
- 适合医疗决策、临床研究等需要可靠推理的场景。
大型语言模型（LLMs）在医学考试中已达到与人类专家相当的水平，但在复杂临床决策中仍面临挑战，因其需深入医学知识理解，而当前多数研究局限于标准化试题场景。主流方法为微调模型，但需大量数据和算力，且易产生‘幻觉’。本文提出RiskAgent，通过整合数百个经验证的循证医学临床决策工具，实现通用且可信的风险预测。实验表明，RiskAgent在跨疾病、多场景的临床风险预测任务中表现优异，且在外部MedCalc-Bench数据集上展现出强大的工具学习泛化能力，在MedQA、MedMCQA和MMLU三个基准上也具备出色的医学推理与问答能力。
原文摘要 · Abstract (English)
Large Language Models (LLMs) achieve competitive results compared to human experts in medical examinations. However, it remains a challenge to apply LLMs to complex clinical decision-making, which requires a deep understanding of medical knowledge and differs from the standardized, exam-style scenarios commonly used in current efforts. A common approach is to fine-tune LLMs for target tasks, which, however, not only requires substantial data and computational resources but also remains prone to generating `hallucinations'. In this work, we present RiskAgent, which synergizes language models with hundreds of validated clinical decision tools supported by evidence-based medicine, to provide generalizable and faithful recommendations. Our experiments show that RiskAgent not only achieves superior performance on a broad range of clinical risk predictions across diverse scenarios and diseases, but also demonstrates robust generalization in tool learning on the external MedCalc-Bench dataset, as well as in medical reasoning and question answering on three representative benchmarks, MedQA, MedMCQA, and MMLU.
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