CHIL 2025举办8场圆桌会，聚焦医疗AI关键挑战与协作方向
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
- 组织8场小规模圆桌，围绕医疗与机器学习交叉议题展开深度对话
- 涵盖可解释性、公平性、因果推理等8个前沿主题，汇聚19位主持人
- 适合关注医疗AI伦理、跨领域协作及实际落地的研究者与从业者
第六届健康推断与学习会议（CHIL 2025）由健康学习与推断协会（AHLI）主办，于2025年6月25日至27日在加利福尼亚大学伯克利分校举行。作为本届会议的重要环节，我们举办了8场研究圆桌会，旨在推动在机器学习与医疗交叉领域的关键、紧迫议题上的协作式小群体对话。每场圆桌由资深与青年联合主持人引导，促进开放交流、思想探索与包容参与。会议强调对核心挑战的严谨讨论、新兴机遇的挖掘以及可行动研究方向的集体构思。圆桌主题包括：可解释性、可理解性与透明性，不确定性、偏见与公平性，因果推理，领域自适应，基础模型，小样本医疗数据学习，多模态方法，以及可扩展的临床转化解决方案。
原文摘要 · Abstract (English)
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions."
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