Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem
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Abstract
Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.
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Public datasets may be prone to biases and may not represent or report the age, race, or sex of the original population.
“Public datasets may be prone to biases and may not represent or report the age, race, or sex of the original population (25).”
From Mitigating Bias in Radiology Machine Learning: 1. Data Handling (2022) · cites this paper as [25]
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Mitigating Bias in Radiology Machine Learning: 1. Data Handling
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Understanding Biases and Disparities in Radiology AI Datasets: A Review
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Multimodal explainability via latent shift applied to COVID-19 stratification
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- Authors
- Garcia Santa Cruz, B., Bossa, M. N., Sölter, J., & Husch, A. D.
- Affiliations
- Vrije Universiteit Brussel (BE)
- University of Luxembourg (LU)
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 74
- Pages
- 102225
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC8479314
- OpenAlex
- W3204899787
- Cited by
- 89
- References
- 16
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 18 · 2023: 27 · 2022: 19 · 2021: 10
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-nd)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Funders
- Fonds National de la Recherche (COVID-19/2020-1/ 14702831 /AICovIX/Husch, PRIDE17/ 12244779/PARK-QC)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- COVID-19 pandemic, chest radiography, public datasets, model bias, risk of bias, data quality assessment, computer-aided diagnosis, clinical translation, confounding, data selection
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, Radiography, X-Rays, Bias
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