医生视角看AI可解释性：如何让医疗AI真正有用且不扰临床工作。
Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
- 通过访谈医生，从临床实际出发提炼AI工具需求。
- 医生担忧影响工作流程和医患关系，培训是关键成功因素。
- 强调先建立通用标准再测试具体工具，适合医疗AI设计者参考。
可解释人工智能（XAI）有望推动基于AI的工具在医疗等高风险场景中的应用与采纳。然而，当前多数研究缺乏终端用户——临床医生的参与，实用性受限。为此，我们对多位临床医生进行了半结构化访谈，探讨他们对AI工具的看法、期望与顾虑。受访医生总体支持临床AI的发展，但关注其如何融入现有工作流程及对医患关系的影响。研究进一步指出，对医生进行AI培训是实现医疗AI成功的关键，并明确了临床医生对（可解释）AI工具的核心期待。与以往研究不同，本工作采用整体性和探索性视角，在测试具体工具前，率先识别出适用于医疗领域的（可解释）AI产品的一般性要求。
原文摘要 · Abstract (English)
Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However, most of the current research lacks input from end users, and therefore their practical value is limited. To address this, we conducted semi-structured interviews with clinicians to discuss their thoughts, hopes, and concerns. Clinicians from our sample generally think positively about developing AI-based tools for clinical practice, but they have concerns about how these will fit into their workflow and how it will impact clinician-patient relations. We further identify training of clinicians on AI as a crucial factor for the success of AI in healthcare and highlight aspects clinicians are looking for in (X)AI-based tools. In contrast to other studies, we take on a holistic and exploratory perspective to identify general requirements for (X)AI products for healthcare before moving on to testing specific tools.
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