Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138

Clinical characteristics of 138 hospitalized patients with 2019 Novel Coronavirus-Infected pneumonia in Wuhan, China

Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Machine Learning under Malware Attack · doi:10.1007/978-3-658-40442-0_9 · Cited by 19,272

GAN（Generative Adversarial Nets）

(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270

La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19

Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463

The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072

QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies

Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00136 · Cited by 10,349

A guide to machine learning for biologists

Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127

Leakage and the reproducibility crisis in machine-learning-based science

Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717

To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis

Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022) · Organization Science · doi:10.1287/orsc.2021.1549 · Cited by 632

Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review

Celi, L. A., Cellini, J., Charpignon, M.-L., Dee, E. C., Dernoncourt, F., Eber, R., Mitchell, W. G., Moukheiber, L., Schirmer, J., Situ, J., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000022 · Cited by 576

Towards a standard for identifying and managing bias in artificial intelligence

Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022) · doi:10.6028/nist.sp.1270 · Cited by 548

Health system-scale language models are all-purpose prediction engines

Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06160-y · Cited by 460

CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII

Kocak, B., Baessler, B., Bakas, S., Cuocolo, R., Fedorov, A., Maier-Hein, L., Mercaldo, N., Müller, H., Orlhac, F., Pinto dos Santos, D., et al. (2023) · Insights into Imaging · doi:10.1186/s13244-023-01415-8 · Cited by 471

A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries

Luo, K., Chen, X., Zheng, H., & Shi, Z. (2022) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.06.049 · Cited by 292

The importance of being external. methodological insights for the external validation of machine learning models in medicine

Cabitza, F., Campagner, A., Soares, F., García de Guadiana-Romualdo, L., Challa, F., Sulejmani, A., Seghezzi, M., & Carobene, A. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106288 · Cited by 284

AI pitfalls and what not to do: mitigating bias in AI

Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277

Artificial Intelligence in Digital Pathology: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01106-8 · Cited by 290

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01049-7 · Cited by 250

The Fallacy of AI Functionality

Raji, I. D., Kumar, I. E., Horowitz, A., & Selbst, A. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533158 · Cited by 251

Accelerating antibiotic discovery through artificial intelligence

Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234

Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594087 · Cited by 204

The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool

Resnik, D. B., & Hosseini, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00493-8 · Cited by 228

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Huang, J., Galal, G., Etemadi, M., & Vaidyanathan, M. (2022) · JMIR Medical Informatics · doi:10.2196/36388 · Cited by 186

Brain–phenotype models fail for individuals who defy sample stereotypes

Greene, A. S., Shen, X., Noble, S., Horien, C., Hahn, C. A., Arora, J., Tokoglu, F., Spann, M. N., Carrión, C. I., Barron, D. S., et al. (2022) · Nature · doi:10.1038/s41586-022-05118-w · Cited by 167

A medical multimodal large language model for future pandemics

Liu, F., Zhu, T., Wu, X., Yang, B., You, C., Wang, C., Lu, L., Liu, Z., Zheng, Y., Sun, X., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00952-2 · Cited by 155

Mitigating Bias in Radiology Machine Learning: 1. Data Handling

Rouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Vera Garcia, D. V., Singh, Y., Zhang, K., Conte, G. M., & Erickson, B. J. (2022) · Radiology: Artificial Intelligence · doi:10.1148/ryai.210290 · Cited by 145

Understanding and Mitigating Bias in Imaging Artificial Intelligence

Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024) · RadioGraphics · doi:10.1148/rg.230067 · Cited by 138

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies

Ebrahimian, S., Kalra, M. K., Agarwal, S., Bizzo, B. C., Elkholy, M., Wald, C., Allen, B., & Dreyer, K. J. (2022) · Academic Radiology · doi:10.1016/j.acra.2021.09.002 · Cited by 120

Is AI leading to a reproducibility crisis in science?

Ball, P. (2023) · Nature · doi:10.1038/d41586-023-03817-6 · Cited by 106

Sounds of COVID-19: exploring realistic performance of audio-based digital testing

Han, J., Xia, T., Spathis, D., Bondareva, E., Brown, C., Chauhan, J., Dang, T., Grammenos, A., Hasthanasombat, A., Floto, A., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00553-x · Cited by 90

A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

Groen, A. M., Kraan, R., Amirkhan, S. F., Daams, J. G., & Maas, M. (2022) · European Journal of Radiology · doi:10.1016/j.ejrad.2022.110592 · Cited by 89

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

Wang, X., Barrera, C., Bera, K., Viswanathan, V. S., Azarianpour-Esfahani, S., Koyuncu, C., Velu, P., Feldman, M. D., Yang, M., Fu, P., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn3966 · Cited by 89

Public Covid-19 X-ray datasets and their impact on model bias – A systematic review of a significant problem

Garcia Santa Cruz, B., Bossa, M. N., Sölter, J., & Husch, A. D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102225 · Cited by 89

Explainable AI for COVID-19 CT Classifiers: An Initial Comparison Study

Ye, Q., Xia, J., & Yang, G. (2021) · 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms52027.2021.00103 · Cited by 83

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina, P., Kaczorek, M. R., Hofmaenner, D. A., Pisciotta, W., Refinetti, P., Singer, M., Mazomenos, E. B., & Whittle, J. (2024) · Anesthesiology · doi:10.1097/aln.0000000000004764 · Cited by 81

Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias

Gallifant, J., Zhang, J., del Pilar Arias Lopez, M., Zhu, T., Camporota, L., Celi, L. A., & Formenti, F. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2021.09.025 · Cited by 72

Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?

London, A. J. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100622 · Cited by 76

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability

Choi, Y., Yu, W., Nagarajan, M. B., Teng, P., Goldin, J. G., Raman, S. S., Enzmann, D. R., Kim, G. H. J., & Brown, M. S. (2023) · RadioGraphics · doi:10.1148/rg.220105 · Cited by 65

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening

Soltan, A. A. S., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M. A., Thickett, D. R., Fairhead, R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00272-7 · Cited by 54

Moving towards vertically integrated artificial intelligence development

Zhang, J., Budhdeo, S., William, W., Cerrato, P., Shuaib, H., Sood, H., Ashrafian, H., Halamka, J., & Teo, J. T. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00690-x · Cited by 54

Towards Regulatory-Compliant MLOps: Oravizio’s Journey from a Machine Learning Experiment to a Deployed Certified Medical Product

Granlund, T., Stirbu, V., & Mikkonen, T. (2021) · SN Computer Science · doi:10.1007/s42979-021-00726-1 · Cited by 53

Machine Learning for Health: Algorithm Auditing & Quality Control

Oala, L., Murchison, A. G., Balachandran, P., Choudhary, S., Fehr, J., Leite, A. W., Goldschmidt, P. G., Johner, C., Schörverth, E. D. M., Nakasi, R., et al. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01783-y · Cited by 48

Machine learning modeling practices to support the principles of AI and ethics in nutrition research

Thomas, D. M., Kleinberg, S., Brown, A. W., Crow, M., Bastian, N. D., Reisweber, N., Lasater, R., Kendall, T., Shafto, P., Blaine, R., et al. (2022) · Nutrition & Diabetes · doi:10.1038/s41387-022-00226-y · Cited by 48

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

Chen, Z., Liu, X., Yang, Q., Wang, Y.-J., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.1671 · Cited by 43

Current State and Prospects of Artificial Intelligence in Allergy

van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Custovic, A., & Koppelman, G. H. (2023) · Allergy · doi:10.1111/all.15849 · Cited by 43

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests

Roland, T., Böck, C., Tschoellitsch, T., Maletzky, A., Hochreiter, S., Meier, J., & Klambauer, G. (2022) · Journal of Medical Systems · doi:10.1007/s10916-022-01807-1 · Cited by 42

National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence

Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., et al. (2023) · Radiographics · doi:10.1148/rg.230180 · Cited by 35

Multimodal explainability via latent shift applied to COVID-19 stratification

Guarrasi, V., Tronchin, L., Albano, D., Faiella, E., Fazzini, D., Santucci, D., & Soda, P. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110825 · Cited by 31

Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology

Kim, Y., Park, J. Y., Hwang, E. J., Lee, S. M., & Park, C. M. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-1342 · Cited by 31

Deep Morphology Learning Enhances Ex Vivo Drug Profiling-Based Precision Medicine

Heinemann, T., Kornauth, C., Severin, Y., Vladimer, G. I., Pemovska, T., Hadzijusufovic, E., Agis, H., Krauth, M.-T., Sperr, W. R., Valent, P., et al. (2022) · Blood Cancer Discovery · doi:10.1158/2643-3230.bcd-21-0219 · Cited by 28

Proactive vs Reactive Machine Learning in Health Care: Lessons From the COVID-19 Pandemic

Luo, Y., Wunderink, R. G., & Lloyd-Jones, D. (2022) · JAMA · doi:10.1001/jama.2021.24935 · Cited by 24

AI in Combating the COVID-19 Pandemic

Cao, L. (2022) · IEEE Intelligent Systems · doi:10.1109/mis.2022.3164313 · Cited by 22

Integrating explainable machine learning in clinical decision support systems: study involving a modified design thinking approach

Shulha, M., Hovdebo, J., D’Souza, V., Thibault, F., & Harmouche, R. (2024) · JMIR Formative Research · doi:10.2196/50475 · Cited by 22

Why an overreliance on AI-driven modelling is bad for science

Narayanan, A., & Kapoor, S. (2025) · Nature · doi:10.1038/d41586-025-01067-2 · Cited by 21

Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study

Sivanesan, U., Wu, K., McInnes, M. D. F., Dhindsa, K., Salehi, F., & van der Pol, C. B. (2023) · Canadian Association of Radiologists Journal · doi:10.1177/08465371221134056 · Cited by 19

I Find Your Lack of Uncertainty in Computer Vision Disturbing

Valdenegro-Toro, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00139 · Cited by 15

Development and validation of a multimodal-based prognosis and intervention prediction model for COVID-19 patients in a multicenter cohort

Lee, J. H., Ahn, J. S., Chung, M. J., Jeong, Y. J., Kim, J. H., Lim, J. K., Kim, J. Y., Kim, Y. J., Lee, J. E., & Kim, E. Y. (2022) · Sensors · doi:10.3390/s22135007 · Cited by 9

When AI is wrong: addressing liability challenges in women's healthcare

Marotta, A. (2022) · Journal of Computer Information Systems · doi:10.1080/08874417.2022.2089773 · Cited by 7

Pulmonary disease classification using globally correlated maximum likelihood: An auxiliary attention mechanism for convolutional neural networks

Verenich, E., Martin, T., Velasquez, A., Khan, N., & Hussain, F. (2021) · arXiv · doi:10.48550/arxiv.2109.00573 · Cited by 4

On Data Bias and the Usability of Deep Learning Algorithms in Classifying COVID-19 based on Chest X-ray

Ezzeddine, H., Awad, M., Ghanem, A. S. A., & Mourani, B. (2021) · 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET) · doi:10.1109/imcet53404.2021.9665574 · Cited by 2