A User Interface to Communicate Interpretable AI Decisions to Radiologists
A User Interface to Communicate Interpretable AI Decisions to Radiologists
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向放射科医生传达可解释的人工智能决策的用户界面
DOI：
10.1117/12.2654068
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Rudin, Cynthia
中科院分区：
文献类型：
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作者：
Ou, Yanchen Jessie;Barnett, Alina J.;Mitra, Anika;Schwartz, Fides R.;Chen, Chaofan;Grimm, Lars;Lo, Joseph Y.;Rudin, Cynthia
基于深度学习的计算机辅助诊断工具在医学领域变得越来越重要。这些工具可能是有用的，但需要有效的沟通他们的决策过程，以安全和有意义的指导临床决策。固有的可解释模型为每个决策提供了与其内部决策过程相匹配的解释。我们提出了一个包含乳腺病变可解释人工智能算法（IAIA-BL）模型的用户界面，该模型可解释地预测乳腺病变的肿块边缘和恶性程度。用户界面显示了模型解释中最相关的方面，包括预测的边际值、预测中的人工智能置信度，以及每种情况下两个激活程度最高的原型。此外，该用户界面还包括感兴趣区域的全景和裁剪图像，以及适合读者研究的问卷。我们的初步结果表明，该模型增加了读者对边缘和恶性肿瘤决策的信心和准确性。
Tools for computer-aided diagnosis based on deep learning have become increasingly important in the medical field. Such tools can be useful, but require effective communication of their decision-making process in order to safely and meaningfully guide clinical decisions. Inherently interpretable models provide an explanation for each decision that matches their internal decision-making process. We present a user interface that incorporates the Interpretable AI Algorithm for Breast Lesions (IAIA-BL) model, which interpretably predicts both mass margin and malignancy for breast lesions. The user interface displays the most relevant aspects of the model’s explanation including the predicted margin value, the AI confidence in the prediction, and the two most highly activated prototypes for each case. In addition, this user interface includes full-field and cropped images of the region of interest, as well as a questionnaire suitable for a reader study. Our preliminary results indicate that the model increases the readers’ confidence and accuracy in their decisions on margin and malignancy.