Cited by 141Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and EducationCOVID-19 diagnosis using AI

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CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis

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Xie, Yao, Chen, Melody, Kao, David, Gao, Ge, Chen, Xiang 'Anthony'·2020·Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

DOI 10.1145/3313831.3376807

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Abstract

The recent development of data-driven AI promises to automate medical diagnosis; however, most AI functions as 'black boxes' to physicians with limited computational knowledge. Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain — a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: (i) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed; (ii) a low-fidelity prototype co-designed with three physicians formulates eight key features; and (iii) a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI. We summarize by discussing recommendations for future work to design and implement explainable medical AI systems that encompass four recurring themes: motivation, constraint, explanation, and justification.

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Authors: Xie, Y., Chen, M., Kao, D., Gao, G., & Chen, X. '.
Affiliations: University of California, Los Angeles (US)
University of Maryland, College Park (US)
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Publisher: ACM
Published: 2020-04-21
Pages: 1-13
DOI: 10.1145/3313831.3376807
OpenAlex: W2999134777
Cited by: 141
References: 40
Citations by year: 2026: 15 · 2025: 27 · 2024: 35 · 2023: 17 · 2022: 27 · 2021: 20
FWCI: 6.10
Type: conference paper
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: NSF (IIS-1850183)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Keywords: explainable AI, chest X-ray interpretation, diagnostic imaging, radiologists, automated medical diagnosis, prototype testing, clinical decision support

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