临床 AI 评估框架：从治理、证据到部署后表现
医院信息科和采购决策者可以收藏这份评估清单，四个维度逐项对照厂商材料，比单看 demo 演示靠谱得多。
该文章提出一套评估临床 AI 的实用框架，覆盖治理、证据、透明度和部署后表现四个维度。核心观点是：demo 演示无法证明模型在真实临床流程中的可靠性，采购方必须审查厂商是否提供持续的性能监测证据。
框架建议医疗机构在引入 AI 工具前建立评估清单，包括数据来源、验证数据集、与现有工作流的集成方式以及上线后的错误追踪机制。对医院信息科和临床决策者而言，这套框架可直接用于供应商尽职调查和采购评审。
A practical framework for assessing AI governance, evidence, transparency and post-deployment performance in clinical workflows.
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include 临床AI, AI治理, 医疗AI, 模型评估.
Source attribution: curated from Fierce Healthcare.
