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Claims of AI outperforming medical practitioners are under scrutiny, as the evidence supporting many of these claims is not convincing or transparently reported. These claims often lack specificity, contextualization, and empirical grounding. In this comment, we offer constructive ethical guidance that can benefit authors, journal editors, and peer reviewers when reporting and evaluating findings in studies comparing AI to physician performance. The guidance provided here forms an essential addition to current reporting guidelines for healthcare studies using machine learning.

Abstract BackgroundImage-driven specialisms such as radiology and pathology are at the forefront of medical artificial intelligence (AI) innovation. Many believe that AI will lead to significant shifts in professional roles, so it is vital to investigate how professionals view the pending changes that AI innovation will initiate and incorporate their views in ongoing AI developments. ObjectiveOur study aimed to gain insights into the perspectives and wishes of radiologists and pathologists regarding the promise of AI. MethodsWe have conducted the first qualitative interview study investigating the perspectives of both radiologists and pathologists regarding the integration of AI in their fields. The study design is in accordance with the consolidated criteria for reporting qualitative research (COREQ). ResultsIn total, 21 participants were interviewed for this study (7 pathologists, 10 radiologists, and 4 computer scientists). The interviews revealed a diverse range of perspectives on the impact of AI. Respondents discussed various task-specific benefits of AI; yet, both pathologists and radiologists agreed that AI had yet to live up to its hype. Overall, our study shows that AI could facilitate welcome changes in the workflows of image-driven professionals and eventually lead to better quality of care. At the same time, these professionals also admitted that many hopes and expectations for AI were unlikely to become a reality in the next decade. ConclusionsThis study points to the importance of maintaining a “healthy skepticism” on the promise of AI in imaging specialisms and argues for more structural and inclusive discussions about whether AI is the right technology to solve current problems encountered in daily clinical practice.