Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System

TL;DR

NaviPath is a human-AI collaborative system that significantly improves pathologists' efficiency and accuracy in navigating high-resolution tumor images by integrating domain knowledge and HCI considerations.

Contribution

The paper introduces NaviPath, a novel human-AI system designed based on pathologists' navigation strategies, enhancing pathology image analysis.

Findings

Participants saw more than twice the number of patterns with NaviPath.

Higher precision and recall achieved with NaviPath compared to manual navigation.

Navigation was more consistent and examination quality improved.

Abstract

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NaviPath -- a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.