1 research outputs found
xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence
Data-driven AI promises support for pathologists to discover sparse tumor
patterns in high-resolution histological images. However, from a pathologist's
point of view, existing AI suffers from three limitations: (i) a lack of
comprehensiveness where most AI algorithms only rely on a single criterion;
(ii) a lack of explainability where AI models tend to work as 'black boxes'
with little transparency; and (iii) a lack of integrability where it is unclear
how AI can become part of pathologists' existing workflow. Based on a formative
study with pathologists, we propose two designs for a human-AI collaborative
tool: (i) presenting joint analyses of multiple criteria at the top level while
(ii) revealing hierarchically traceable evidence on-demand to explain each
criterion. We instantiate such designs in xPath -- a brain tumor grading tool
where a pathologist can follow a top-down workflow to oversee AI's findings. We
conducted a technical evaluation and work sessions with twelve medical
professionals in pathology across three medical centers. We report quantitative
and qualitative feedback, discuss recurring themes on how our participants
interacted with xPath, and provide initial insights for future physician-AI
collaborative tools.Comment: 31 pages, 11 figure