论文标题
从为病理学家设计AI支持AI的诊断工具中学到的经验教训
Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists
论文作者
论文摘要
尽管有数据驱动的人工智能（AI）的承诺，但对于我们如何在传统医师驱动的诊断和AI自动化的医学的合理未来之间弥合海湾知之甚少。具体来说，鉴于大多数AI仍然是新生且容易出错（例如，在数字病理学中），我们如何将AI参与医师的诊断工作流程？为了探讨这个问题，我们首先提出了一系列协作技术，以通过AI的能力和局限性吸引人类病理学家，基于我们原型的动力，这是一种工具 - 一种工具，其中AI采取各种程度的举措，为从组织学滑动的肿瘤检测肿瘤方面提供各种形式的援助。我们总结了与八位病理学家的研究中学到的观察结果和经验教训，并讨论了以人为中心的医学AI系统工作的建议。
Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI usefully in physicians' diagnosis workflow given that most AI is still nascent and error-prone (e.g., in digital pathology)? To explore this question, we first propose a series of collaborative techniques to engage human pathologists with AI given AI's capabilities and limitations, based on which we prototype Impetus - a tool where an AI takes various degrees of initiatives to provide various forms of assistance to a pathologist in detecting tumors from histological slides. We summarize observations and lessons learned from a study with eight pathologists and discuss recommendations for future work on human-centered medical AI systems.
