基于语言模型的序贯诊断
Sequential Diagnosis with Language Models
AI总结：
本文提出序贯诊断基准和MAI-DxO编排器，将NEJM-CPC病例转化为迭代诊断场景，通过模拟医生小组策略性选择高价值检查，使o3诊断准确率提升至80%并显著降低成本。
摘要：
人工智能在扩大专家医学知识和推理的可及性方面展现出巨大前景。然而，大多数语言模型评估依赖静态病例片段和多项选择题，无法反映现实环境中循证医学的复杂性与细微差别。在临床实践中，医生会迭代地形成并修正诊断假设，根据刚刚获得的信息调整后续问题和检查，并在做出最终诊断前权衡不断变化的证据。为模拟这一迭代过程，我们引入了序贯诊断基准（Sequential Diagnosis Benchmark），将304个具有诊断挑战性的《新英格兰医学杂志》临床病理讨论会（NEJM-CPC）病例转化为逐步诊断交互场景。医生或AI从简短的病例摘要开始，必须迭代地向一个“守门人”模型请求额外细节，该模型仅在明确查询时才揭示检查结果。性能评估不仅依据诊断准确率，还依据医生访视和所执行检查的成本。我们还提出了MAI诊断编排器（MAI-DxO），这是一个与模型无关的编排器，模拟医生小组，提出可能的鉴别诊断并策略性地选择高价值、高成本效益的检查。与OpenAI的o3模型配合使用时，MAI-DxO达到80%的诊断准确率——是普通医生20%平均准确率的四倍。与医生相比，MAI-DxO还将诊断成本降低了20%，与现成的o3相比降低了70%。在配置为追求最高准确率时，MAI-DxO达到85.5%的准确率。MAI-DxO带来的这些性能提升在OpenAI、Gemini、Claude、Grok、DeepSeek和Llama系列模型中具有普适性。我们强调，当AI系统被引导以迭代方式思考并审慎行动时，能够在临床诊疗中提升诊断精度和成本效益。
英文摘要：
Artificial intelligence holds great promise for expanding access to expert medical knowledge and reasoning. However, most evaluations of language models rely on static vignettes and multiple-choice questions that fail to reflect the complexity and nuance of evidence-based medicine in real-world settings. In clinical practice, physicians iteratively formulate and revise diagnostic hypotheses, adapting each subsequent question and test to what they've just learned, and weigh the evolving evidence before committing to a final diagnosis. To emulate this iterative process, we introduce the Sequential Diagnosis Benchmark, which transforms 304 diagnostically challenging New England Journal of Medicine clinicopathological conference (NEJM-CPC) cases into stepwise diagnostic encounters. A physician or AI begins with a short case abstract and must iteratively request additional details from a gatekeeper model that reveals findings only when explicitly queried. Performance is assessed not just by diagnostic accuracy but also by the cost of physician visits and tests performed. We also present the MAI Diagnostic Orchestrator (MAI-DxO), a model-agnostic orchestrator that simulates a panel of physicians, proposes likely differential diagnoses and strategically selects high-value, cost-effective tests. When paired with OpenAI's o3 model, MAI-DxO achieves 80% diagnostic accuracy--four times higher than the 20% average of generalist physicians. MAI-DxO also reduces diagnostic costs by 20% compared to physicians, and 70% compared to off-the-shelf o3. When configured for maximum accuracy, MAI-DxO achieves 85.5% accuracy. These performance gains with MAI-DxO generalize across models from the OpenAI, Gemini, Claude, Grok, DeepSeek, and Llama families. We highlight how AI systems, when guided to think iteratively and act judiciously, can advance diagnostic precision and cost-effectiveness in clinical care.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
