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While artificial intelligence (AI) algorithms continue to rival humanperformance on a variety of clinical tasks, the question of how best toincorporate these algorithms into clinical workflows remains relativelyunexplored. We investigated how AI can affect pathologist performance on thetask of differentiating between two subtypes of primary liver cancer,hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). We developed an AIdiagnostic assistant using a deep learning model and evaluated its effect onthe diagnostic performance of eleven pathologists with varying levels ofexpertise. Our deep learning model achieved an accuracy of 0.885 on an internalvalidation set of 26 slides and an accuracy of 0.842 on an independent test setof 80 slides. Despite having high accuracy on a hold out test set, thediagnostic assistant did not significantly improve performance acrosspathologists (p-value: 0.184, OR: 1.287 (95% CI 0.886, 1.871)). Modelcorrectness was observed to significantly bias the pathologist decisions. Whenthe model was correct, assistance significantly improved accuracy across allpathologist experience levels and for all case difficulty levels (p-value: <0.001, OR: 4.289 (95% CI 2.360, 7.794)). When the model was incorrect,assistance significantly decreased accuracy across all 11 pathologists and forall case difficulty levels (p-value < 0.001, OR: 0.253 (95% CI 0.126, 0.507)).Our results highlight the challenges of translating AI models to the clinicalsetting, especially for difficult subspecialty tasks such as tumorclassification. In particular, they suggest that incorrect model predictionscould strongly bias an expert's diagnosis, an important factor to consider whendesigning medical AI-assistance systems.

Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma  and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic  Assistant