The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“They showed that XAI provides implications for how physicians can explore and understand data-driven, AIenabled medical imaging analysis to assist physicians in the medical decision-making process.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [116]
In the citing paper
“XAI could help the user understand when to trust a model and why an error may occur [97,116].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [116]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
A theory of diagnosis from first principles
Reiter, R. (1987) · Artificial Intelligence · doi:10.1016/0004-3702(87)90062-2 · Cited by 3,031
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685
The Inevitable Application of Big Data to Health Care
Murdoch, T. B., & Detsky, A. S. (2013) · JAMA · doi:10.1001/jama.2013.393 · Cited by 1,676
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Fong, R. C., & Vedaldi, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.371 · Cited by 1,097
Research works printed but not linked: 26
- Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y. Lim, and Mohan Kankanhalli. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18). ACM, NY, NY, USA, 582:1--582:18.
- Acr. 2010. ACR Practice Guideline for Communication of Diagnostic Imaging Findings. 1076, Revised 2008 (2010), 1--6.
- 10.1001/jama.1987.03400010071030 · DOI
- O. Biran and C. Cotton. 2017. Explanation and justification in machine learning: A survey. IJCAI-17 Workshop on Explainable AI (XAI) (2017).
- Piotr Dabkowski and Yarin Gal. 2017. Real time image saliency for black box classifiers. In Advances in Neural Information Processing Systems. 6967--6976.
- Anind K. Dey and Alan Newberger. 2009. Support for context-aware intelligibility and control. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 859--868.
- Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Paishun Ting Karthikeyan Shanmugam and Payel Das. 2018. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems. 592--603.
- Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. (Feb. 2017).
- Wenjuan Fan, Jingnan Liu, Shuwan Zhu, and Panos M Pardalos. 2018. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann. Oper. Res. (March 2018).
- M. Fieschi. 2013. Artificial Intelligence in Medicine: Expert Systems. Springer.
- Nicholas Frosst and Geoffrey Hinton. 2017. Distilling a Neural Network Into a Soft Decision Tree. (Nov. 2017).
- W. James Murdoch and Arthur Szlam. 2017. Automatic Rule Extraction from Long Short Term Memory Networks. (Feb. 2017).
- Saif Khairat, David Marc, William Crosby, and Ali Al Sanousi. 2018. Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis. JMIR Med Inform 6, 2 (April 2018), e24.
- Zachary C. Lipton. 2016. The Mythos of Model Interpretability. (June 2016).
- Rodrigues Mark and Qureshi Zeshan. 2017. The Unofficial Guide to Radiology: Chest, Abdominal and Orthopaedic X Rays, Plus CTs, MRIs and Other Important Modalities: Core Radiology Curriculum. (2017).
- Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. 2017. A simple neural network module for relational reasoning. In Advances in Neural Information Processing Systems 30, I Guyon, U V Luxburg, S Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 4967--4976.
- Sectra. 2013. How radiology can improve communication with referring physicians. June (2013).
- Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning Important Features Through Propagating Activation Differences. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML'17). JMLR.org, Sydney, NSW, Australia, 3145--3153.
- William R Swartout. 1983. Xplain: A system for creating and explaining expert consulting programs. Technical Report. UNIVERSITY OF SOUTHERN CALIFORNIA MARINA DEL REY INFORMATION SCIENCES INST.
- William van Melle, Edward H Shortliffe, and Bruce G Buchanan. 1984. EMYCIN: A knowledge engineer's tool for constructing rule-based expert systems. Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project (1984), 302--313.
- Tianfu Wu, Wei Sun, Xilai Li, Xi Song, and Bo Li. 2017. Towards Interpretable R-CNN by Unfolding Latent Structures. (Nov. 2017).
- Qian Yang, John Zimmerman, Aaron Steinfeld, Lisa Carey, and James F. Antaki. 2016. Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help. ACM Trans. Comput. Hum. Interact. 2016 (May 2016), 4477--4488.
- Xie Yao, Ge Gao, and Xiang 'anthony' Chen. 2019. Outlining the Design Space of Explainable Intelligent Systems for Medical Diagnosis. (Feb. 2019).
- Matthew D. Zeiler and Rob Fergus. 2014. Visualizing and Understanding Convolutional Networks. In Computer Vision -- ECCV 2014. Springer International Publishing, 818--833.
- Qiao Zheng, Hervé Delingette, and Nicholas Ayache. 2019. Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow. Med. Image Anal. 56 (Aug. 2019), 80--95.
- Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, and Max Welling. 2017. Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. (Feb. 2017).
Cited by
Sorted by citations
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629
Chen, H., Gomez, C., Huang, C.-M., & Unberath, M. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00699-2 · Cited by 300
Borys, K., Schmitt, Y. A., Nauta, M., Seifert, C., Krämer, N. C., Friedrich, C. M., & Nensa, F. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110787 · Cited by 186
Jacobs, M., He, J., F. Pradier, M., Lam, B., Ahn, A. C., McCoy, T. H., Perlis, R. H., Doshi-Velez, F., & Gajos, K. Z. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445385 · Cited by 177
Panigutti, C., Beretta, A., Giannotti, F., & Pedreschi, D. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3502104 · Cited by 162
Metadata
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
