云边协同系统让农村医院用AI高效筛查多种疾病。
A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings
- 边缘设备用轻量模型处理原始医疗数据，云端大模型整合结果。
- 诊断工具召回率98%~99%，延迟稳定在25~35秒，耗能降低4~15倍。
- 适合网络差、算力弱的偏远医疗场景，提升多模态诊断效率。
医疗AI已达到专家级诊断准确率，但在资源匮乏的农村地区，受限于带宽不足、计算能力弱，且需融合多种异构医学模态，其应用仍面临挑战。本文提出一种云-边协同架构：边缘侧部署轻量、领域专用模型，将原始医疗数据转化为紧凑结构化输出；云端大语言模型（LLM）据此生成临床摘要。一个基于LLM的调度器可根据患者情况动态选择诊断工具，确保全面覆盖多模态信息，避免无效处理。在20个涵盖心脏、产科、创伤和筛查场景的多模态病例上，模拟三种网络环境（500kbps–5Mbps）测试。该混合系统实现98%–99%诊断工具召回率，92%–96%精确率，临床准确性不低于或优于纯云端基线，且保持带宽无关的延迟（25–35秒），令牌成本降低4–15倍。结果表明，合理的系统设计可有效实现多模态融合，在部署约束下提升事实一致性。
原文摘要 · Abstract (English)
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.
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