Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology
This addresses workflow integration issues for pathologists, offering incremental improvements in human-AI collaboration for medical diagnosis.
The paper tackled the challenge of integrating AI tools into pathologists' workflows by developing xPath, a human-AI collaborative diagnosis system that mimics pathologists' examination processes, with viability confirmed through technical evaluation and sessions with twelve medical professionals.
Recent developments in AI have provided assisting tools to support pathologists' diagnoses. However, it remains challenging to incorporate such tools into pathologists' practice; one main concern is AI's insufficient workflow integration with medical decisions. We observed pathologists' examination and discovered that the main hindering factor to integrate AI is its incompatibility with pathologists' workflow. To bridge the gap between pathologists 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. The viability of xPath is confirmed by a technical evaluation and work sessions with twelve medical professionals in pathology. This work identifies and addresses the challenge of incorporating AI models into pathology, which can offer first-hand knowledge about how HCI researchers can work with medical professionals side-by-side to bring technological advances to medical tasks towards practical applications.