CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis
| Autor: | Xiang 'Anthony' Chen, Ge Gao, Yao Xie, David T. H. Kao, Melody Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science 05 social sciences Computer Science - Human-Computer Interaction 020207 software engineering 02 engineering and technology Data science 3. Good health Data-driven Human-Computer Interaction (cs.HC) Summative assessment 0202 electrical engineering electronic engineering information engineering Medical imaging Systems design 0501 psychology and cognitive sciences Medical diagnosis 050107 human factors H.5.m |
| Zdroj: | CHI |
| Popis: | 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: (1) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed; (2) a low-fidelity prototype co-designed with three physicians formulates eight key features; and (3) 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. 10 pages, 5 figures |
| Databáze: | OpenAIRE |
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