逆向的医生-AI关系：由大型语言模型驱动的全流程临床诊断
Reverse Physician-AI Relationship: Full-process Clinical Diagnosis Driven by a Large Language Model
AI总结：
针对AI仅作为医生助手无法驱动全流程诊断的问题，提出DxDirector-7B模型，将AI作为主导者、医生为助手，在实现高诊断准确率的同时大幅减少医生工作量并建立误诊问责框架。
摘要：
现实世界中的全流程临床诊断涵盖了从仅有的模糊主诉开始的整个诊断工作流程。尽管人工智能（AI），特别是大型语言模型（LLMs），正在改变临床诊断，但其角色在很大程度上仍然是医生的助手。这种AI辅助的工作模式使得AI只能在诊断过程中的某些环节回答特定的医学问题，而缺乏从模糊主诉开始驱动整个诊断过程的能力，这仍然严重依赖人类医生。这一差距限制了AI充分减轻医生工作量和提高诊断效率的能力。为了解决这个问题，我们提出了一种范式转变，即逆转医生与AI之间的关系：将AI重新定位为主要主导者，而医生作为其助手。因此，我们提出了DxDirector-7B，这是一种具备高级深度思考能力的LLM，使其能够在最少医生参与的情况下驱动全流程诊断。此外，DxDirector-7B为误诊建立了一个稳健的问责框架，界定了AI与人类医生之间的责任。在全流程诊断设置下对罕见、复杂和真实世界病例的评估中，DxDirector-7B不仅取得了显著优越的诊断准确性，而且与最先进的医疗LLM以及通用LLM相比，大幅减少了医生的工作量。跨多个临床科室和任务的细粒度分析验证了其有效性，专家评估表明其具有作为医学专家可行替代方案的潜力。这些发现标志着一个新时代的到来：AI不再仅仅是医生的助手，而是驱动整个诊断过程以大幅减少医生工作量，提供了一种高效且准确的诊断解决方案。
英文摘要：
Full-process clinical diagnosis in the real world encompasses the entire diagnostic workflow that begins with only an ambiguous chief complaint. While artificial intelligence (AI), particularly large language models (LLMs), is transforming clinical diagnosis, its role remains largely as an assistant to physicians. This AI-assisted working pattern makes AI can only answer specific medical questions at certain parts within the diagnostic process, but lack the ability to drive the entire diagnostic process starting from an ambiguous complaint, which still relies heavily on human physicians. This gap limits AI's ability to fully reduce physicians' workload and enhance diagnostic efficiency. To address this, we propose a paradigm shift that reverses the relationship between physicians and AI: repositioning AI as the primary director, with physicians serving as its assistants. So we present DxDirector-7B, an LLM endowed with advanced deep thinking capabilities, enabling it to drive the full-process diagnosis with minimal physician involvement. Furthermore, DxDirector-7B establishes a robust accountability framework for misdiagnoses, delineating responsibility between AI and human physicians. In evaluations across rare, complex, and real-world cases under full-process diagnosis setting, DxDirector-7B not only achieves significant superior diagnostic accuracy but also substantially reduces physician workload than state-of-the-art medical LLMs as well as general-purpose LLMs. Fine-grained analyses across multiple clinical departments and tasks validate its efficacy, with expert evaluations indicating its potential to serve as a viable substitute for medical specialists. These findings mark a new era where AI, traditionally a physicians' assistant, now drives the entire diagnostic process to drastically reduce physicians' workload, indicating an efficient and accurate diagnostic solution.
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。
