PatientVLM 与 DocVLM 相遇：为高效诊断的预咨询对话
PatientVLM Meets DocVLM: Pre-Consultation Dialogue Between Vision-Language Models for Efficient Diagnosis
AI总结：
本文提出预咨询对话框架，通过视觉语言模型模拟医生与患者对话，利用合成症状提升诊断效率。
摘要：
传统上，医学诊断中的AI研究主要集中在图像分析上。尽管这带来了显著的进步，但缺乏患者报告的症状仍然阻碍了诊断的准确性。为此，我们提出了预咨询对话框架（PCDF），该框架模仿现实世界的诊断流程，医生在得出结论前会逐步向患者提问。具体来说，我们模拟了两个视觉语言模型（VLM）之间的诊断对话：DocVLM 根据图像和对话历史生成后续问题，而 PatientVLM 则根据真实诊断生成的症状资料进行回应。我们还对由该框架生成的合成症状进行了小规模的临床验证，有执照的临床医生确认了其临床相关性、症状覆盖性和整体真实性。这些发现表明，由此产生的 DocVLM-PatientVLM 互动形成了与图像和诊断配对的连贯多轮咨询，我们随后用这些对话来微调 DocVLM。基于对话的监督在图像-only 训练上带来了显著的提升，突显了真实症状获取在诊断中的价值。
英文摘要：
Traditionally, AI research in medical diagnosis has largely centered on image analysis. While this has led to notable advancements, the absence of patient-reported symptoms continues to hinder diagnostic accuracy. To address this, we propose a Pre-Consultation Dialogue Framework (PCDF) that mimics real-world diagnostic procedures, where doctors iteratively query patients before reaching a conclusion. Specifically, we simulate diagnostic dialogues between two vision-language models (VLMs): a DocVLM, which generates follow-up questions based on the image and dialogue history, and a PatientVLM, which responds using a symptom profile derived from the ground-truth diagnosis. We additionally conducted a small-scale clinical validation of the synthetic symptoms generated by our framework, with licensed clinicians confirming their clinical relevance, symptom coverage, and overall realism. These findings indicate that the resulting DocVLM-PatientVLM interactions form coherent, multi-turn consultations paired with images and diagnoses, which we then use to fine-tune the DocVLM. This dialogue-based supervision leads to substantial gains over image-only training, highlighting the value of realistic symptom elicitation for diagnosis.
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。
