发表机构
University of Michigan; Massachusetts Institute of Technology; Harvard Medical School（密歇根大学; 麻省理工学院; 哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对资源受限农村地区的多模态临床筛查问题，提出云边协同架构，经实验验证其诊断性能优异且成本更低，可适配部署约束。
AI 中文摘要
医疗AI已展现出专家级诊断准确率，但在带宽稀缺、计算受限且临床决策需整合异质模态的资源受限农村地区，这类能力大多仍无法获取。我们提出一种云边协同架构以应对这些约束：边缘端的轻量领域专用模型将原始医疗数据转换为紧凑结构化输出，云端的大语言模型（LLM）将这些输出合成为临床总结。基于LLM的编排器会根据患者情境动态选择诊断工具，在不处理无关输入的前提下提升多模态覆盖度。我们在涵盖心脏、产科、创伤及筛查场景的20个多模态临床病例上，针对三种模拟网络配置（500kbps至5Mbps）开展评估。该混合系统的诊断工具召回率达98%-99%，精确率为92%-96%，临床准确率与纯云基线相当或更优，且在延迟保持带宽不变（25-35秒）的同时，令牌成本降低4-15倍。这些结果凸显了架构设计在部署约束下实现高效多模态整合、提升相较于纯云方法事实依据的作用。
英文摘要
Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural settings where bandwidth is scarce, compute is limited, and clinical decision-making requires integrating heterogeneous modalities. We introduce a cloud--edge collaborative architecture that addresses these constraints: lightweight, domain-specific models on the edge transform raw medical data into compact structured outputs, while a cloud LLM synthesizes these outputs into clinical summaries. An LLM-based orchestrator dynamically selects diagnostic tools based on patient context, promoting comprehensive modality coverage without processing irrelevant inputs. We evaluate on 20 multimodal clinical cases spanning cardiac, obstetric, trauma, and screening scenarios under three simulated network profiles (500,kbps--5,Mbps). The hybrid system achieves 98--99% diagnostic tool recall with 92--96% precision, matches or exceeds cloud-only baselines on clinical accuracy, and maintains bandwidth-invariant latency (25--35,s) at 4--15x lower token cost. These results highlight the role of architectural design in enabling efficient multimodal integration and improving factual grounding compared to cloud-only approaches under deployment constraints.
Comments31 pages, 3 figures. In Proceedings of Machine Learning Research, Volume 340, 2026 (Machine Learning for Healthcare Conference)
Journal refProceedings of the 11th Machine Learning for Healthcare Conference (MLHC), PMLR 340:283-313, 2026
