用大模型+逻辑编程实现可解释的疾病诊断。
A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming
- 大模型将医学文献转为逻辑代码，结合患者数据推理
- 小规模任务上表现良好，诊断结果可追溯来源
- 适合需要透明决策过程的医疗场景
准确的疾病预测对及时干预、有效治疗和减少并发症至关重要。尽管符号AI已在医疗中应用，但高质量知识库构建成本高，限制了其普及。本文提出McCoy框架，结合大语言模型（LLMs）与答案集编程（ASP），利用LLM将医学文献自动转化为ASP代码，与患者数据融合后，由ASP求解器生成最终诊断。该方法整合两类技术优势，构建出鲁棒且可解释的预测系统。初步实验表明，McCoy在小规模疾病诊断任务中表现优异。
原文摘要 · Abstract (English)
Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-quality knowledge bases. This work introduces McCoy, a framework that combines Large Language Models (LLMs) with Answer Set Programming (ASP) to overcome this barrier. McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis. This integration yields a robust, interpretable prediction framework that leverages the strengths of both paradigms. Preliminary results show McCoy has strong performance on small-scale disease diagnosis tasks.
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