為何醫生使用可解釋AI時準確率會下降
麻省理工學院研究人員發現，ai可解釋性工具通常能提高患者的準確性，但會降低醫生的表現。這表明醫療ai介面需要根據用戶的專業知識水平進行動態調整。
mit researchers and collaborators evaluated ai explainability tools within the context of skin disease diagnosis, discovering that their utility varies drastically by user expertise. non-expert users demonstrated improved accuracy by deferring to the model, but primary care providers frequently experienced reduced performance because they were misled by the explanations. the study indicates that standard explainability methods can disrupt the clinical judgment of skilled professionals. this signals a shift away from static tools toward adaptive systems that account for the user's baseline medical knowledge. future ai deployments in healthcare must customize interfaces to support, rather than confuse, expert clinicians.