AI for radiographic COVID-19 detection selects shortcuts over signal
Open accessPublished version of a preprint
Abstract
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals. We observe that the approach to obtain training data for these AI systems introduces a nearly ideal scenario for AI to learn these spurious ‘shortcuts’. Because this approach to data collection has also been used to obtain training data for the detection of COVID-19 in computed tomography scans and for medical imaging tasks related to other diseases, our study reveals a far-reaching problem in medical-imaging AI. In addition, we show that evaluation of a model on external data is insufficient to ensure AI systems rely on medically relevant pathology, because the undesired ‘shortcuts’ learned by AI systems may not impair performance in new hospitals. These findings demonstrate that explainable AI should be seen as a prerequisite to clinical deployment of machine-learning healthcare models. The urgency of the developing COVID-19 epidemic has led to a large number of novel diagnostic approaches, many of which use machine learning. DeGrave and colleagues use explainable AI techniques to analyse a selection of these approaches and find that the methods frequently learn to identify features unrelated to the actual disease.
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Almost all studies report demographic and non-demographic characteristics of the study population, such as age, sex, ethnicity, socioeconomic status, disease severity, and comorbidities.
“Almost all studies report demographic and non-demographic characteristics of the study population, such as age, sex, ethnicity, socioeconomic status, disease severity, and comorbidities, and some studies also include information on geographical location and study site.”
From The medical algorithmic audit (2022) · cites this paper as [32]
In “Evaluation of models on new hospital systems,” the paper recommends routine reporting of metadata on patient and hospital-system confounds; it does not assess how often other studies report population characteristics. “Shortcuts have a variable effect on generalization” discusses patient sex in the authors’ own datasets. Searches for age, ethnicity, socioeconomic status, disease severity, comorbidities, and demographic reporting found no passage supporting the claim that almost all studies report these characteristics. (LLM, conf=0.75)
Experimental — accuracy not yet measuredDeep learning models on chest X-ray image datasets were not relying on medical pathology but instead on confounding factors for COVID-19 detection.
“It was demonstrated that deep learning models on chest X-ray image datasets were not relying on medical pathology but instead on confounding factors for COVID-19 detection [41,43].”
From Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks (2023) · cites this paper as [43]
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- Authors
- DeGrave, A. J., Janizek, J. D., & Lee, S.-I.
- Affiliations
- University of Washington (US)
- Corresponding author
- Lee, Su-In
- Venue
- Nature Machine Intelligence
- Published
- 2021-05-31
- Volume
- 3
- Issue
- 7
- Pages
- 610-619
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC7523163
- OpenAlex
- W3171849353
- Cited by
- 631
- References
- 18
- Citations by year
- 2026: 134 · 2025: 87 · 2024: 116 · 2023: 126 · 2022: 102 · 2021: 59 · 2020: 6 · 2019: 1
- FWCI
- 53.63
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion, other-oa)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- NSF | BIO | Division of Biological Infrastructure (DBI-1552309)
- U.S. Department of Health & Human Services | National Institutes of Health (R35 GM 128638, R01 AG 061132)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 detection, chest radiograph, shortcut learning, spurious correlations, confounding factors, explainable AI, machine learning robustness, external validation, training data collection, medical imaging AI, clinical implementation of AI
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