推动医疗机器人与AI协同发展,构建国家级创新中心
Final Report for the Workshop on Robotics & AI in Medicine
- 汇聚多方力量共谋医疗机器人与AI融合的国家愿景
- 共识建立国家级中心,聚焦安全可靠与临床转化落地
- 关注偏远、灾祸及军事场景,推动智能医疗普惠化
2025年12月1日,印第安纳波利斯举办的医疗机器人与人工智能研讨会(CARE Workshop)召集了顶尖科研人员、临床医生、产业创新者及联邦机构代表,共同制定医疗领域推进机器人与人工智能发展的国家蓝图。会议强调工程创新需与真实临床需求衔接,重点提升安全性、可靠性与转化准备度。通过主旨报告、圆桌讨论和分组研讨,与会者指出数据稀缺、评估标准缺失、监管路径不清及人才培养不足等关键障碍,制约智能机器人系统在手术、诊断、康复与辅助场景中的部署。会议重申人工智能赋能机器人在精准性、减轻医护负担、扩大优质医疗可及性方面的巨大潜力,尤其在资源匮乏地区和高风险操作中。特别关注战地、灾难救援等严苛环境。与会者达成广泛共识:亟需设立国家医疗人工智能与机器人卓越中心(CARE)。优先研究方向包括人机协作、可信自主、仿真与数字孪生、多模态感知,以及生成式AI在临床流程中的伦理整合。各方呼吁建设高质量数据集、共享测试平台、自主外科系统、临床基准与持续跨学科培训机制。
原文摘要 · Abstract (English)
The CARE Workshop on Robotics and AI in Medicine, held on December 1, 2025 in Indianapolis, convened leading researchers, clinicians, industry innovators, and federal stakeholders to shape a national vision for advancing robotics and artificial intelligence in healthcare. The event highlighted the accelerating need for coordinated research efforts that bridge engineering innovation with real clinical priorities, emphasizing safety, reliability, and translational readiness with an emphasis on the use of robotics and AI to achieve this readiness goal. Across keynotes, panels, and breakout sessions, participants underscored critical gaps in data availability, standardized evaluation methods, regulatory pathways, and workforce training that hinder the deployment of intelligent robotic systems in surgical, diagnostic, rehabilitative, and assistive contexts. Discussions emphasized the transformative potential of AI enabled robotics to improve precision, reduce provider burden, expand access to specialized care, and enhance patient outcomes particularly in undeserved regions and high risk procedural domains. Special attention was given to austere settings, disaster and relief and military settings. The workshop demonstrated broad consensus on the urgency of establishing a national Center for AI and Robotic Excellence in medicine (CARE). Stakeholders identified priority research thrusts including human robot collaboration, trustworthy autonomy, simulation and digital twins, multi modal sensing, and ethical integration of generative AI into clinical workflows. Participants also articulated the need for high quality datasets, shared test beds, autonomous surgical systems, clinically grounded benchmarks, and sustained interdisciplinary training mechanisms.
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