Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI usefully in physicians' diagnosis workflow given that most AI is still nascent and error-prone (e.g., in digital pathology)? To explore this question, we first propose a series of collaborative techniques to engage human pathologists with AI given AI's capabilities and limitations, based on which we prototype Impetus - a tool where an AI takes various degrees of initiatives to provide various forms of assistance to a pathologist in detecting tumors from histological slides. We summarize observations and lessons learned from a study with eight pathologists and discuss recommendations for future work on human-centered medical AI systems.
@article{arxiv.2006.12695,
title = {Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists},
author = {Hongyan Gu and Jingbin Huang and Lauren Hung and Xiang 'Anthony' Chen},
journal= {arXiv preprint arXiv:2006.12695},
year = {2021}
}
Comments
25 pages, 5 figures. To appear in the 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2021)