KnowBench: Effort Reduction as a Unified, Deployment-Grounded Benchmark for Clinical AI· 临床AI新基准：Effort Reduction
Clinical AI systems are evaluated with instruments built for research settings (reference-based similarity metrics and expert rubric panels) that measure resemblance to an artifact rather than reduction of a burden. We introduce KnowBench, pioneered by Knowtex, whose unifying metric is Effort Reduction (ER): the proportion of system-generated clinical work product accepted by the responsible clinician under expert and safety review. ER is defined once and instantiated per task across the administrative workload clinical AI automates: visit notes, diagnosis and billing codes, orders, EHR chart summarization, patient after-visit summaries, and clinical decision support. In every instantiation the construction is identical: the clinician's review-and-attestation event is the ground truth, eve
临床AI评估新基准：Effort Reduction，衡量系统减轻医生工作负担的能力。
- 核心方法
- 提出Effort Reduction（ER）作为统一指标，通过专家和安全审查下医生对系统生成的临床工作产品的接受比例来衡量。
- 适合谁读
- 研究者、工程师、医疗产品开发人员
- 要解决的问题
- 现有的临床AI系统评估方法侧重于与专家标准的相似度，而非实际工作中的效率提升。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种全新的、以部署为导向的临床AI系统评估方法，适用于多种临床任务。
- 意义与局限
- 意义在于更贴近实际临床应用，评估AI系统的真实价值；可能影响未来临床AI系统的开发方向和评估标准；局限在于具体实施的复杂性和审查过程的标准化。