REMEDY：视频生成距离医学教育世界模型还有多远？
REMEDY: How Far Is Video Generation from Medical Education World Models?
浏览论文内容
中文总结 AI 辅助
本文提出首个医学教学演示生成基准REMEDY，评估五个视频生成模型在四个场景12项任务上的表现，发现视觉质量不等于临床正确性，最先进模型严格临床成功率仅28.25%，为未来医学世界模型指明方向。
中文摘要 AI 辅助
近期的视频生成模型能够生成逼真的视频，并展现出作为世界模型基础的潜力。这些进展为生成医学教学演示创造了机会，而这既需要令人信服的视觉质量，也需要精确的程序性动作。然而，当前生成器能否满足这些要求尚未得到衡量。为解决这一问题，我们引入了医学教育演示合成就绪度评估（REMEDY），据我们所知，这是首个针对AI生成医学教学演示的基准。REMEDY提供了来自真实演示视频的900个首帧，涵盖四个场景中的12项任务：手术室、影像、临床与床边以及复苏。五个当代开源视频生成模型基于这些帧生成了4500个视频。我们将特定任务的临床检查表与视频和运动质量指标相结合。评估涵盖四个维度：临床动作遵循、临床特征、视频质量和运动质量。我们的结果表明，逼真的外观和时间一致性并不能确保正确的临床动作。即使最先进的MiniMax-H3在严格的临床成功率上也仅达到28.25%，细粒度的临床动作仍然具有挑战性。这些发现为开发未来的医学教育世界模型奠定了基础并提供了路线图。
英文摘要
Recent video generation models produce realistic videos and show potential as a foundation for world models. These advances create opportunities for generating medical teaching demonstrations, which requires both convincing visual quality and precise procedural actions. However, whether current generators can meet these requirements has not been measured. To address this problem, we introduce Readiness Evaluation of Medical Education Demonstration sYnthesis (REMEDY), to our knowledge, the first benchmark for AI-generated medical teaching demonstrations. REMEDY provides 900 first frames from real demonstration videos, covering 12 tasks across four scenarios: operating room, imaging, clinic and bedside, and resuscitation. Five contemporary open-source video generation models produce 4,500 videos from these frames. We combine task-specific clinical checklists with video and motion quality metrics. Evaluation covers four dimensions: clinical action following, clinical profiles, video quality, and motion quality. Our results show that realistic appearance and temporal consistency do not ensure correct clinical actions. Even the most advanced MiniMax-H3 achieves only 28.25% on strict clinical success rate, and fine-grained clinical actions remain challenging. These findings establish a foundation and roadmap for developing future medical education world models.
