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

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.

Accepted ACM CHI Conference on Human Factors in Computing Systems (CHI '23)

References in corpus (9)

• To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
• Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes
• "Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment
• Survey of XAI in digital pathology
• BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
• Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data
• GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks
• Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
• Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge

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