SCoT把多轮专家会诊搬进模型协作，在难诊断病例上比单模型召回更高，做医疗AI的可以看看。
OpenAI 2026年报告显示，全球超过5%的ChatGPT消息与医疗健康相关。研究者提出Social Chain of Thought（SCoT），一个面向医学鉴别诊断的多轮协作推理管道。在与单智能体基线、单智能体管道消融和best-of-n扩展的对比中，SCoT的召回优势无法靠单体推理复现。在最难诊断的病例上，多轮专科对话能恢复真实诊断并收敛到更高召回率的鉴别结果。
Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology
Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users. When OpenAI (2026) reports that more than 5% of ChatGPT messages globally are healthcare-related, the transparency of these systems becomes a serious design concern. This is especially true for complex cases, where differential diagnosis often requires integrating multiple forms of specialist reasoning. Existing work has proposed multi-agent approaches to medical diagnosis, but it remains unclear when such systems are needed, why they help, and where they outperform monolithic inference. We introduce Social Chain of Thought (SCoT),a multi-round pipeline for medical differential diagnosis that structures multi-agent interaction as a deliberative framework for collabora. tive LLM reasoning. Evaluating SCoT against single-agent baselines, one-agent pipeline ablations, and best-of-n scaling, we show that its recall advantage is not reproduced by monolithic inference alone. SCoT is most successful in the hardest diagnostic cases, where multiple rounds of specialist conversation help recover ground-truth diagnoses and converge on a higher-recall differential.