mlsanityData to 7 Oct 2026

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

arXiv 2001.05149 ↗First posted 15 Jan 2020cs.HCcited by 40

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Abstract (arXiv)

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.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2020.

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Citing papers per year: 2021: 12; 2022: 7; 2023: 8; 2024: 6; 2025: 4; 2026: 3.

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  1. 2021Human-Centered Explainable AI (XAI): From Algorithms to User Experiencescited by 123
  2. 2021Fair Conformal Predictors for Applications in Medical Imagingcited by 74
  3. 2021Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Makingcited by 58
  4. 2022Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AIcited by 40
  5. 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38
  6. 2021Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactionscited by 35
  7. 2023Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAIcited by 33
  8. 2021Question-Driven Design Process for Explainable AI User Experiencescited by 31
  9. 2021Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Reviewcited by 30
  10. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Imagescited by 30
  11. 2021Teaching Humans When To Defer to a Classifier via Exemplarscited by 29
  12. 2023Selective Explanations: Leveraging Human Input to Align Explainable AIcited by 29
28 more
  1. 2022Uncalibrated Models Can Improve Human-AI Collaborationcited by 26
  2. 2022Seamful XAI: Operationalizing Seamful Design in Explainable AIcited by 20
  3. 2023XAIR: A Framework of Explainable AI in Augmented Realitycited by 17
  4. 2021The human-AI relationship in decision-making: AI explanation to support people on justifying their decisionscited by 9
  5. 2021Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputscited by 9
  6. 2024Comparing Zealous and Restrained AI Recommendations in a Real-World Human-AI Collaboration Taskcited by 8
  7. 2022An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiographcited by 7
  8. 2025There Was Never a Bottleneck in Concept Bottleneck Modelscited by 7
  9. 2024Towards Multi-dimensional Explanation Alignment for Medical Classificationcited by 6
  10. 2023The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologiescited by 5
  11. 2023Explainable AI And Visual Reasoning: Insights From Radiologycited by 5
  12. 2021Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretabilitycited by 4
  13. 2021Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructurecited by 3
  14. 2025Causal Disentanglement for Robust Long-tail Medical Image Generationcited by 2
  15. 2025Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Predictioncited by 2
  16. 2021Deploying clinical machine learning? Consider the following...cited by 1
  17. 2022Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infantscited by 1
  18. 2024MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Modelscited by 1
  19. 2024"It depends": Configuring AI to Improve Clinical Usefulness Across Contextscited by 1
  20. 2022Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcarecited by 0
  21. 2022Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Userscited by 0
  22. 2023A Meta-heuristic Approach to Estimate and Explain Classifier Uncertaintycited by 0
  23. 2023Using ScrutinAI for Visual Inspection of DNN Performance in a Medical Use Casecited by 0
  24. 20243DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaborationcited by 0
  25. 2025Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-makingcited by 0
  26. 2026Within the MDT Room: Situated in Multidisciplinary Team-Grounded Agent Debate for Clinical Diagnosiscited by 0
  27. 2026A User-Centric Analysis of Explainability in AI-Based Medical Image Diagnosiscited by 0
  28. 2026Co-Annotator: Expert-Distilled ViT and VLM for Visual and Documentation Guidance in Age-Related Macular Degenerationcited by 0