Improving Workflow Integration with xPath: Design and Evaluation of a Human-AI Diagnosis System in Pathology
Hongyan Gu, Yuan Liang, Yifan Xu, Christopher Kazu Williams, Shino Magaki, Négar Khanlou, Harry V. Vinters, Zesheng Chen, Shuo Ni, Chunxu Yang, Wenzhong Yan, Xinhai R. Zhang, Yang Li, Mohammad Haeri, Xiang Chen
Abstract
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 12 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.