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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
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Abstract Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.
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“Much of the current evidence is based on retrospective analyses with limited prospective validation [19,28].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [28]
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“The success of deep learning in computer vision (CV) analysis has significantly advanced its clinical applications in comprehending biomedical images, including CT scans [88], positron emission tomography (PET)-CT scans [89], whole-slide images (WSIs) [90], and MRI scans [91].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [88]
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“Based on this study, we suggest that such a gold rush may give rise to un-engaged 'augmentation' which is highly risky from a learning and knowledge perspective for experts, AI companies, and consumers.”
From To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis (2022) · cites this paper as Roberts et al. 2021
In the citing paper
“Despite problems like the one discovered by Roberts et al. [195], the fact that ML systems process and analyze data differently from human beings can be a great benefit to science and society because these systems may be able to identify useful and innovative structures, properties, patterns, and relationships that human beings would not recognize.”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [195]
In the citing paper
“Differences in image acquisition and processing may result in “shortcuts” where models take cues from non-clinical features.”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [15]
In the citing paper
“For example, COVID-19 prediction models have been demonstrated to learn which institution the images were obtained from rather than features pertaining to the underlying disease. 15”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [15]
In the citing paper
“The methodological flaws likely is a reflection of the lack of diversity in the development team to allow clinicians, data analysts and patient advocacy groups to work side-by-side throughout the AI life cycle. 15”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [15]
In the citing paper
“A similar result is summarized in a systematic review of COVID-19 prediction models on CT scans and Chest X-rays (CXRs) that found limited clinical utility due to methodological flaws and underlying biases stemming from small training data sets, data set variability, and limited integration of non-imaging data, among others.”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [15]
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A Unified Multi-Task Vision Transformer for Interpretable Ovarian Tumour Analysis
Musa, A. A., Emmanuel, D., Hassan, A. A., & Fernando, A. (2026) · Electronics · doi:10.3390/electronics15163662
Velicki, L., Milovančev, A., Preveden, A., Vučković, J., Belopavlovic, M., Rodić, M., Filipović, N., & Jakovljević, D. G. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15166313 · Cited by 1
“Medical AI studies have repeatedly shown that limited external validation and dataset weaknesses can produce exaggerated performance estimates [32,34].”
Comparative Evaluation of Classical and Transfer Learning Models for MRI-Based ACL Tear Detection
Singh, E., Malhotra, R., & Mor, S. (2026) · IEEE Guwahati Subsection Conference (GCON) · doi:10.1109/gcon69192.2026.11649015
“In every experiment cut is assumed to be 70, 15, and 15 percent, i.e., training, validation, and testing respectively because of leakage at the slice level [13].”
Babu, A. M., Nehemiah, A. J., Jagadeep, V., Suji, R. A., & Nallusamy, P. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1890072
“…dataset-and site-specific: the ranking above relies on selected benchmarks and often does not hold up to scrutiny when confronted with external unselected populations, as the inability of many hundreds of published imaging models for COVID-19 to produce a single clinically useful one shows (60).”
Kızılkurtlu, A. A., & Akpek, A. (2026) · Biosensors · doi:10.3390/bios16080448 · Cited by 1
Ramirez, C., Torres, E., Ortega, P., & Mendes, S. (2026) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/ppht0m5a9h
Meyer, L., Schmid, A., Braun, S., & Keller, L. (2026) · International Journal Of Pharmaceutical Research And Allied Sciences · doi:10.51847/ugyerdbsld
Adebusuyi, O., Ibekwe, J. P., Otubu, R., Ajadi, A., Aderinto, T., Sola-Alao, O. E., Babalola, A. V., Obagaye, J., & Lukman, B. O. (2026) · doi:10.2196/preprints.109367
Lemke, T., Marka, A. W., Prucker, P., Komenda, A., Weller, D., Mertens, C. J., Kim, S. H., Lammert, J., Huber, T., Graf, M. M., et al. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.08.014
Noori, G. H., Bibi, S., & Lee, S. W. (2026) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580261478793 · Cited by 3
“13 Adoption of AI by radiologists has been affected by human factors, and acceptability varies depending on workflow integration, confidence calibration, and interface design in along with raw accuracy.”
Sharma, A. (2026) · Research Square · doi:10.21203/rs.3.rs-10794678/v1
Standards, Regulation, and Governance for Agentic AI
Piroumian, V. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-18585-3_13
Firdaus, M. N. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7331360
Phiri, J., Zimba, A., Njovu, C., & Lumpa, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01958-x
Alhaj, Z. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02073-7
“This prevents patient-level leakage between training and validation sets when repeated acquisitions are available from the same patient [59].”
Integration of artificial intelligence and multi-omics for precision medicine
Marei, H. E. S., Cenciarelli, C., & Vagni, D. (2026) · Functional & Integrative Genomics · doi:10.1007/s10142-026-02024-6
“…literature found all 232 identified models at high or unclear risk of bias despite reported discrimination up to 0.99 (Wynants et al. 2020; Roberts et al. 2021), traceable largely to reporting gaps that instruments such as PROBAST (Wolff et al. 2019 ) and PROBAST + AI (Moons et al. 2025)…”
InfluentialVaussenat, F., Bhattacharya, A., Payette, J., Desmettre, T., Saïdi, A., Gagnon, G., & Cloutier, S. G. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1849571
“In medical imaging, KD has been applied to compress diagnostic models for edge deployment (Roberts et al., 2021), but few studies have used KD to train from-scratch architectures that compete with pretrained baselines.”
Mitrovic, P. M., Rajic, D., & Djordjevic, N. (2026) · Echocardiography · doi:10.1111/echo.70610
Miao, Y., Zhang, X., Leng, F., & Zhu, Y. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1881041
“Differences in scanner vendors, exposure parameters, acquisition procedures, image quality, disease prevalence, and patient characteristics may alter performance substantially (23).”
Alecu, Ş., & Onea, G. A. (2026) · AI · doi:10.3390/ai7090343
Tasnim, S., Xue, Y., & Abdu, N. (2026) · IRBM · doi:10.1016/j.irbm.2026.100960
Stahl, R., Stüber, A. T., Wania, R., Ingrisch, M., Ataabadi, M. O., Öchsner, M., Forbrig, R., Trumm, C. G., Liebig, T., Böcker, W., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16172840
“…far more readily than rigorous negative results, and reviews of the field have repeatedly found that the large majority of published models are undermined by methodological shortcomings, optimistic evaluation, and limited reproducibility, leaving few, if any, that are clinically usable [41,42].”
Hussaini, H., Bano, S., Elyan, E., & Moreno‐García, C. F. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02249-8
Metadata
- Authors
- Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., Weir-McCall, J. R., Teng, Z., Gkrania-Klotsas, E., AIX-COVNET, Ruggiero, A., Korhonen, A., Jefferson, E., Ako, E., Langs, G., Gozaliasl, G., Yang, G., Prosch, H., Preller, J., Stanczuk, J., Tang, J., Hofmanninger, J., Babar, J., Sánchez, L. E., Thillai, M., Gonzalez, P. M., Teare, P., Zhu, X., Patel, M., Cafolla, C., Azadbakht, H., Jacob, J., Lowe, J., Zhang, K., Bradley, K., Wassin, M., Holzer, M., Ji, K., Ortet, M. D., Ai, T., Walton, N., Lio, P., Stranks, S., Shadbahr, T., Lin, W., Zha, Y., Niu, Z., Rudd, J. H. F., Sala, E., & Schönlieb, C.-B.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- University of Cambridge (GB)
- University of Manchester (GB)
- Royal Papworth Hospital (GB)
- University of Dundee (GB)
- Royal Brompton Hospital (GB)
- Medical University of Vienna (AT)
- University of Helsinki (FI)
- Imperial College London (GB)
- Boehringer Ingelheim (Germany) (DE)
- Addenbrooke's Hospital (GB)
- University College London (GB)
- Macau University of Science and Technology (MO)
- Tongji Hospital (CN)
- Renmin Hospital of Wuhan University (CN)
- Corresponding author
- Roberts, Michael
- ORCID
- Roberts, Michael
- Thorpe, Matthew
- Gilbey, Julian
- Yeung, Michael
- Ursprung, Stephan
- McCague, Cathal
- Weir-McCall, Jonathan R.
- Gkrania-Klotsas, Effrossyni
- Ako, Emmanuel
- Langs, Georg
- Gozaliasl, Ghassem
- Yang, Guang
- Prosch, Helmut
- Preller, Jacobus
- Azadbakht, Hojjat
- Jacob, Joseph
- Ji, Kangyu
- Walton, Nicholas
- Stranks, Samuel
- Shadbahr, Tolou
- Rudd, James H. F.
- Sala, Evis
- Schönlieb, Carola-Bibiane
- Venue
- Nature Machine Intelligence
- Published
- 2021-03-15
- Volume
- 3
- Issue
- 3
- Pages
- 199-217
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3119527628
- Cited by
- 1,044
- References
- 83
- Citations by year
- 2026: 203 · 2025: 96 · 2024: 143 · 2023: 248 · 2022: 224 · 2021: 130
- FWCI
- 98.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- Discovery Research Portal (University of Dundee) (publishedVersion, cc-by)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- elib (German Aerospace Center) (acceptedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- arXiv (Cornell University)
- Funders
- AstraZeneca
- European Commission (777826)
- Medical Research Council (1966157, MR/S010351/1)
- Engineering and Physical Sciences Research Council (EP/T017961/1, EP/N014588/1, EP/S026045/1)
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- COVID-19 pandemic, machine learning, chest radiograph, chest CT, COVID-19 detection, outcome prediction, clinical utility, model validation, methodological errors, bias, research recommendations
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 ›
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