迈向医疗AI科学家
Towards a Medical AI Scientist
AI总结：
本文提出医疗AI科学家框架，通过临床工程师协同推理机制生成可落地的医学研究想法，并基于结构化医学规范和伦理政策撰写论文，验证其在171案例、19临床任务和6种数据模态中的高质量表现。
摘要：
自主系统能够生成科学假设、进行实验和撰写论文，最近成为加速发现的有前景范式。然而，现有AI科学家大多领域无关，限制了其在临床医学中的应用，因为研究必须基于医学证据和专门的数据模态。本文介绍医疗AI科学家，首个针对临床自主研究的框架。它通过临床工程师协同推理机制将广泛调研的文献转化为可操作的证据，提高生成研究想法的可追溯性。它进一步促进基于结构化医学组成规范和伦理政策的证据引导论文撰写。该框架运行于三种研究模式：基于论文的复现、文献启发的创新和任务驱动的探索，每种模式对应不同的自动化科学探究层次。全面评估表明，医疗AI科学家生成的想法在171案例、19临床任务和6种数据模态中均显著优于商业LLM。同时，系统在方法与实现之间实现强一致性，并在可执行实验中表现出显著更高的成功率。双盲评估显示，生成的论文达到MICCAI级质量，同时超过ISBI和BIBM的论文。提出的医疗AI科学家展示了利用AI在医疗领域进行自主科学发现的潜力。
英文摘要：
Autonomous systems that generate scientific hypotheses, conduct experiments, and draft manuscripts have recently emerged as a promising paradigm for accelerating discovery. However, existing AI Scientists remain largely domain-agnostic, limiting their applicability to clinical medicine, where research is required to be grounded in medical evidence with specialized data modalities. In this work, we introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research. It enables clinically grounded ideation by transforming extensively surveyed literature into actionable evidence through clinician-engineer co-reasoning mechanism, which improves the traceability of generated research ideas. It further facilitates evidence-grounded manuscript drafting guided by structured medical compositional conventions and ethical policies. The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration, each corresponding to a distinct level of automated scientific inquiry with progressively increasing autonomy. Comprehensive evaluations by both large language models and human experts demonstrate that the ideas generated by the Medical AI Scientist are of substantially higher quality than those produced by commercial LLMs across 171 cases, 19 clinical tasks, and 6 data modalities. Meanwhile, our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments. Double-blind evaluations by human experts and the Stanford Agentic Reviewer suggest that the generated manuscripts approach MICCAI-level quality, while consistently surpassing those from ISBI and BIBM. The proposed Medical AI Scientist highlights the potential of leveraging AI for autonomous scientific discovery in healthcare.
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。
