让医疗AI在不重训练的情况下，自动适应不同患者和场景。
One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
- 通过上下文切换，在推理时动态调整模型输出，无需重新训练。
- 支持跨专科、跨人群、跨地域的医疗场景适配，提升实用性。
- 适合希望实现通用医疗AI的开发者与医疗机构使用。
医疗AI（包括临床语言模型、视觉-语言模型及多模态健康记录模型）已能总结病历、回答问题并辅助决策。但其在新人群、新专科或新护理环境中的应用常依赖微调、提示工程或外部知识库检索，存在扩展性差和上下文错误风险——输出看似合理却遗漏关键信息。本文提出上下文切换作为解决方案：在推理阶段动态调整模型推理过程，无需重训练。生成模型可依据患者生理特征、诊疗场景或疾病类型定制输出；多模态模型能融合病历、检验结果、影像与基因组数据，即使部分数据缺失或延迟也能推理；智能体模型则可根据任务与用户角色协调工具与职责。上下文切换使医疗AI能灵活适应各种临床情境，推动其向无限场景扩展，同时保持可靠性与现实适用性。这需要数据设计、模型架构与评估框架的协同进步。
原文摘要 · Abstract (English)
Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Their adaptation to new populations, specialties, or care settings often relies on fine-tuning, prompting, or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors: outputs that appear plausible but miss critical patient or situational information. We envision context switching as a solution. Context switching adjusts model reasoning at inference without retraining. Generative models can tailor outputs to patient biology, care setting, or disease. Multimodal models can reason on notes, laboratory results, imaging, and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on tasks and users. In each case, context switching enables medical AI to adapt across specialties, populations, and geographies. It requires advances in data design, model architectures, and evaluation frameworks, and establishes a foundation for medical AI that scales to infinitely many contexts while remaining reliable and suited to real-world care.
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