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  • Title: ➤  What’s In An Explanation?: A Grounded Theory Study To Understand Clinicians’ Views On Explainability In Healthcare Machine Learning
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There is an existing implementation gap in health AI, due in part to concerns about medicolegal accountability and ethical, responsible decision-making when integrating AI into the clinical workflow. Additionally, there is likely some misalignment of the notion of ‘explainability’ between developers and clinicians. There is an urgent need for clarity to align expectations of clinicians with the technical abilities of developers to ensure that any solutions intended to assist interpretation of AI outputs meet the needs of users. Moreover, clinician education should be geared toward areas where there is a misinterpretation or misunderstanding of the technical capabilities of so-called explainability solutions. This study will explore clinicians' expectations, understanding, and hopes for explainability-related needs of AI systems at the point-of-care, specifically as they relate to responsible clinical decision-making. Through a grounded theory approach, we intend to develop a theoretical framework to ground clinical thinking and interaction with AI, providing actionable directions for development, education, and accountability.

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