让医生与AI多轮对话，提升诊断准确性。
MedSyn: Enhancing Diagnostics with Human-AI Collaboration
- 医生与开源大模型进行多轮交互，动态优化诊断。
- 模拟实验表明，大模型能有效辅助医生决策。
- 适合医疗AI协作、临床决策支持研究者。
临床决策复杂，常受认知偏差、信息不全和病例模糊性影响。大型语言模型（LLMs）在辅助临床决策方面展现出潜力，但其通常采用单次或有限交互方式，难以应对真实医疗实践的复杂性。本文提出一种混合人机协作框架MedSyn，使医生与LLM通过多步、交互式对话共同完善诊断与治疗方案。与静态辅助工具不同，MedSyn支持动态交流，医生可质疑模型建议，模型亦能提供替代视角。通过模拟医生-模型互动，评估开源LLMs作为医生助手的潜力。结果表明，开源大模型在真实世界中具备作为医生助手的前景。未来工作将开展真实医生参与的实验，进一步验证MedSyn在诊断准确性和患者预后方面的有效性。
原文摘要 · Abstract (English)
Clinical decision-making is inherently complex, often influenced by cognitive biases, incomplete information, and case ambiguity. Large Language Models (LLMs) have shown promise as tools for supporting clinical decision-making, yet their typical one-shot or limited-interaction usage may overlook the complexities of real-world medical practice. In this work, we propose a hybrid human-AI framework, MedSyn, where physicians and LLMs engage in multi-step, interactive dialogues to refine diagnoses and treatment decisions. Unlike static decision-support tools, MedSyn enables dynamic exchanges, allowing physicians to challenge LLM suggestions while the LLM highlights alternative perspectives. Through simulated physician-LLM interactions, we assess the potential of open-source LLMs as physician assistants. Results show open-source LLMs are promising as physician assistants in the real world. Future work will involve real physician interactions to further validate MedSyn's usefulness in diagnostic accuracy and patient outcomes.
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