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Recent developments in AI have provided assisting tools to supportpathologists' diagnoses. However, it remains challenging to incorporate suchtools into pathologists' practice; one main concern is AI's insufficientworkflow integration with medical decisions. We observed pathologists'examination and discovered that the main hindering factor to integrate AI isits incompatibility with pathologists' workflow. To bridge the gap betweenpathologists and AI, we developed a human-AI collaborative diagnosis tool --xPath -- that shares a similar examination process to that of pathologists,which can improve AI's integration into their routine examination. Theviability of xPath is confirmed by a technical evaluation and work sessionswith twelve medical professionals in pathology. This work identifies andaddresses the challenge of incorporating AI models into pathology, which canoffer first-hand knowledge about how HCI researchers can work with medicalprofessionals side-by-side to bring technological advances to medical taskstowards practical applications.

Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology