TeamPath用AI助手帮病理医生高效诊断，还能跨模态整合数据。
TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots
- 基于强化学习和路由机制，灵活选择最佳分析路径。
- 能准确识别专家结论错误并修正推理过程，提升诊断效率。
- 适合临床病理诊断场景，支持多模态信息融合与专家协作。
人工智能在计算病理学领域取得进展，推动多模态诊断与分析进入新阶段。然而，现有病理视觉语言模型仍缺乏严谨的推理路径和处理多样化任务的能力，限制了其在真实场景中作为AI助手的应用。本文提出TeamPath，一个基于大规模组织病理学多模态数据集，结合强化学习与路由增强方案的AI系统，可作为虚拟助手实现专家级疾病诊断、切片级信息摘要及跨模态生成（如整合转录组数据），服务于临床应用。我们与耶鲁医学院病理科医生合作，验证了TeamPath能有效辅助其工作，识别并纠正专家判断与推理路径中的错误。人机评估结果进一步支持其推理质量。整体上，TeamPath可根据需求动态选择最优配置，成为跨模态与专家间信息沟通的创新可靠系统。
原文摘要 · Abstract (English)
Advances in AI have introduced several strong models in computational pathology to usher it into the era of multi-modal diagnosis, analysis, and interpretation. However, the current pathology-specific visual language models still lack capacities in making the diagnosis with rigorous reasoning paths as well as handling divergent tasks, and thus, challenges of building AI Copilots for real scenarios still exist. Here we introduce TeamPath, an AI system powered by reinforcement learning and router-enhanced solutions based on large-scale histopathology multimodal datasets, to work as a virtual assistant for expert-level disease diagnosis, patch-level information summarization, and cross-modality generation to integrate transcriptomic information for clinical usage. We also collaborate with pathologists from Yale School of Medicine to demonstrate that TeamPath can assist them in working more efficiently by identifying and correcting expert conclusions and reasoning paths. We also discuss the human evaluation results to support the reasoning quality from TeamPath. Overall, TeamPath can flexibly choose the best settings according to the needs, and serve as an innovative and reliable system for information communication across different modalities and experts.
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