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Recommendations for Reporting Machine Learning Analyses in Clinical Research
Open accessAbstract
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.
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“Similar to internal validation of the AIPM, external AIPM model validation can be based on discrimination (area under the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values), calibration (calibration plot)5,6,13,17,49,56,78, and Decision Curve Analysis5,6,17,49,68,72.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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“Code for the complete model building pipeline should be published in well-documented scripts with computer environment requirements when possible7,8,13,14,16,19,20,24,25,27,33,50,62,64,65, including statements about any restrictions to access or re-use.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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“To ensure the generalizability of the AIPM to the intended healthcare setting, developers are advised to extensively validate the model for representative data from that setting6–8,10,13–15,24,27,56,64,66,68,72,77,79,87,88 (see phase 1, Representativeness).”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
In the citing paper
“and is best informed - a priori - by medical expert knowledge or existing literature13,17,76.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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“It has been widely recommended to report any measures taken to prevent overfitting3,7,8,13,15.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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“One commonly referred strategy is feature selection13,15,27,47,76, for which it is explicitly recommended that selection should work independently of model training (unlike in methods as forward and backward selection) and is best informed - a priori - by medical expert knowledge or existing literature13,17,76.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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“for which it is explicitly recommended that selection should work independently of model training (unlike in methods as forward and backward selection)”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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“To facilitate transparency and reproducibility of the developmental process, the used computational architecture, high-performance techniques, software packages, and versioning (data, model, configurations and training scripts) should be reported13,14,16,50,64–66.”
From Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022) · cites this paper as [13]
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Citations
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., Egger, M., & Initiative, F. T. S. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040297 · Cited by 8,613
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
Collins, P. G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.639 · Cited by 3,319
Collins, G. S., Reitsma, J. B., Altman, D. G., & Moons, K. G. M. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0697 · Cited by 3,267
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., Poole, C., Schlesselman, J. J., & Egger, M. (2014) · International Journal of Surgery · doi:10.1016/j.ijsu.2014.07.014 · Cited by 2,969
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710
Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction
Shah, S. J., Katz, D. H., Selvaraj, S., Burke, M. A., Yancy, C. W., Gheorghiade, M., Bonow, R. O., Huang, C.-C., & Deo, R. C. (2015) · Circulation · doi:10.1161/circulationaha.114.010637 · Cited by 1,157
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Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ · doi:10.1136/bmj-2023-078378 · Cited by 4,718
de Hond, A. A. H., Leeuwenberg, A. M., Hooft, L., Kant, I. M. J., Nijman, S. W. J., van Os, H. J. A., Aardoom, J. J., Debray, T. P. A., Schuit, E., van Smeden, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-021-00549-7 · Cited by 499
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Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4653923 · Cited by 321
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Lones, M. A. (2024) · Patterns · doi:10.1016/j.patter.2024.101046 · Cited by 298
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Oikonomou, E. K., & Khera, R. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01985-3 · Cited by 190
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Kolbinger, F. R., Veldhuizen, G. P., Zhu, J., Truhn, D., & Kather, J. N. (2024) · Communications Medicine · doi:10.1038/s43856-024-00492-0 · Cited by 160
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Li, B., Feridooni, T., Cuen-Ojeda, C., Kishibe, T., de Mestral, C., Mamdani, M., & Al-Omran, M. A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00552-y · Cited by 145
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Coan, L. J., Williams, B. M., Adithya, V. K., Upadhyaya, S., Kafri, A. S. A., Czanner, S., Venkatesh, R., Willoughby, C. E., Kavitha, S., & Czanner, G. (2022) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2022.08.005 · Cited by 143
Cai, Y., Cai, Y.-Q., Tang, L.-Y., Wang, Y.-H., Gong, M., Jing, T.-C., Li, H.-J., Li-Ling, J., Hu, W., Yin, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03273-7 · Cited by 122
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Sangha, V., Mortazavi, B. J., Haimovich, A. D., Ribeiro, A. H., Brandt, C., Jacoby, D. L., Schulz, W. L., Krumholz, H. M., Ribeiro, A. L. P., & Khera, R. (2022) · Nature Communications · doi:10.1038/s41467-022-29153-3 · Cited by 120
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Arsiwala-Scheppach, L. T., Chaurasia, A., Müller, A., Krois, J., & Schwendicke, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12030937 · Cited by 108
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
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Huang, C., Li, S., Caraballo, C., Masoudi, F. A., Rumsfeld, J. S., Spertus, J. A., Normand, S. T., Mortazavi, B. J., & Krumholz, H. M. (2021) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.007526 · Cited by 90
Self-citationSvennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P. A., Garcia, R., Ghanbari, H., Hindricks, G., Man, S. H., et al. (2025) · EP Europace · doi:10.1093/europace/euaf071 · Cited by 98
Homeyer, A., Geißler, C., Schwen, L. O., Zakrzewski, F., Evans, T., Strohmenger, K., Westphal, M., Bülow, R. D., Kargl, M., Karjauv, A., et al. (2022) · Modern Pathology · doi:10.1038/s41379-022-01147-y · Cited by 88
Khera, R., Asnani, A. H., Krive, J., Addison, D., Zhu, H., Vasbinder, A. L., Fleming, M. R., Arnaout, R., Razavi, P., & Okwuosa, T. M. (2025) · Circulation Genomic and Precision Medicine · doi:10.1161/hcg.0000000000000097 · Cited by 83
Althouse, A. D., Below, J. E., Claggett, B. L., Cox, N. J., de Lemos, J. A., Deo, R. C., Duval, S., Hachamovitch, R., Kaul, S., Keith, S. W., et al. (2021) · Circulation · doi:10.1161/circulationaha.121.055393 · Cited by 74
Self-citationGolder, S., Xu, D., O’Connor, K., Wang, Y., Batra, M., & Gonzalez‐Hernandez, G. (2025) · Drug Safety · doi:10.1007/s40264-024-01505-6 · Cited by 87
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Barker, J., Li, X., Khavandi, S., Koeckerling, D., Mavilakandy, A. K., Pepper, C., Bountziouka, V., Chen, L., Kotb, A. I., Antoun, I., et al. (2022) · EP Europace · doi:10.1093/europace/euac135 · Cited by 64
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Cai, Y., Gong, D.-X., Tang, L.-Y., Cai, Y. H., Li, H., Jing, T.-C., Gong, M., Hu, W., Zhang, Z.-W., Zhang, X., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/47645 · Cited by 54
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McGuinness, A. J., Stinson, L. F., Snelson, M., Loughman, A., Stringer, A. M., Hannan, A. J., Cowan, C. S. M., Jama, H. A., Caparros-Martin, J. A., West, M. L., et al. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.09.022 · Cited by 44
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Saputro, S. A., Pattanaprateep, O., Pattanateepapon, A., Karmacharya, S., & Thakkinstian, A. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01841-z · Cited by 34
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Ahmad, F. S., Luo, Y., Wehbe, R. M., Thomas, J. D., & Shah, S. J. (2022) · Heart Failure Clinics · doi:10.1016/j.hfc.2021.12.002 · Cited by 34
Yamga, E., Mantena, S., Rosen, D. T., Bucholz, E. M., Yeh, R. W., Celi, L. A., Ustun, B., & Butala, N. M. (2023) · Journal of the American Heart Association · doi:10.1161/jaha.122.029232 · Cited by 34
Hamilton, D. E., Albright, J., Seth, M., Painter, I. S., Maynard, C. C., Hira, R. S., Sukul, D., & Gurm, H. S. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehad836 · Cited by 34
Haymond, S., & Master, S. R. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab272 · Cited by 33
Rainey, C., McConnell, J., Hughes, C., Bond, R., & McFadden, S. L. (2021) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2021.100033 · Cited by 34
Yuan, T., Edelmann, D., Fan, Z., Alwers, E., Kather, J. N., Brenner, H., & Hoffmeister, M. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102589 · Cited by 34
Budiarto, A., Tsang, K. C. H., Wilson, A. M., Sheikh, A., & Shah, S. A. (2023) · JMIR AI · doi:10.2196/46717 · Cited by 34
Bahameish, M., Stockman, T., & Carrión, J. R. (2024) · Sensors · doi:10.3390/s24103210 · Cited by 44
Allareddy, V., Oubaidin, M., Rampa, S., Venugopalan, S. R., Elnagar, M. H., Yadav, S., & Lee, M. K. (2023) · Orthodontics and Craniofacial Research · doi:10.1111/ocr.12721 · Cited by 31
Danilatou, V., Dimopoulos, D., Κωστούλας, Θ., & Douketis, J. D. (2024) · Thrombosis and Haemostasis · doi:10.1055/a-2299-4758 · Cited by 25
Clift, A. K., Tan, P. S., Patone, M., Liao, W., Coupland, C. A. C., Bashford-Rogers, R. J. M., Sivakumar, S., & Hippisley‐Cox, J. (2024) · British Journal of Cancer · doi:10.1038/s41416-024-02693-9 · Cited by 29
Silva, C. G. D. S. E., Buginga, G., Silva, E. D. S. E., Arena, R. A., Rouleau, C. R., Aggarwal, S., Wilton, S. B., Austford, L. D., Hauer, T., & MYERS, J. N. (2022) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2022.01.016 · Cited by 25
Stabellini, N., Cullen, J., Moore, J. X., Dent, S. F., Sutton, A. L., Shanahan, J., Montero, A. J., & Guha, A. (2023) · Cancers · doi:10.3390/cancers15184630 · Cited by 25
de Kok, J. W. T. M., van Rosmalen, F., Koeze, J., Keus, F., van Kuijk, S. M. J., Forte, J. C., Schnabel, R. M., Driessen, R., van Herpt, T. T. W., Sels, J.-W. E. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-51699-z · Cited by 27
Eken, A., Nassehi, F., & Eroğul, O. (2024) · Reviews in the Neurosciences · doi:10.1515/revneuro-2023-0117 · Cited by 25
Symmetric Projection Attractor Reconstruction: Sex Differences in the ECG
Lyle, J. V., Nandi, M., & Aston, P. J. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.709457 · Cited by 23
Wong, J. E., Yamaguchi, M., Nishi, N., Araki, M., & Wee, L. H. (2022) · JMIR Formative Research · doi:10.2196/40404 · Cited by 23
Machine learning in cardiac surgery: a narrative review
Miles, T. J., & Ghanta, R. K. (2024) · Journal of Thoracic Disease · doi:10.21037/jtd-23-1659 · Cited by 21
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Petrušić, I., Savić, A. M., Mitrović, K., Bačanin, N., Sebastianelli, G., Secci, D., & Coppola, G. (2024) · The Journal of Headache and Pain · doi:10.1186/s10194-024-01924-x · Cited by 23
Smith, H., Sweeting, M. J., Morris, T. P., & Crowther, M. J. (2022) · Diagnostic and Prognostic Research · doi:10.1186/s41512-022-00124-y · Cited by 26
Momenzadeh, A., Shamsa, A., & Meyer, J. G. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac063 · Cited by 25
Otokiti, A. U., Ozoude, M. M., Williams, K. S., Sadiq-Onilenla, R. A., Ojo, S. A., Warsame, L., Walsh, S., & Edomwande, M. (2022) · JMIR Research Protocols · doi:10.2196/37685 · Cited by 26
Lorde, N., Mahapatra, S., & Kalaria, T. (2024) · Diagnostics · doi:10.3390/diagnostics14161808 · Cited by 21
Malik, S., Tenorio, B. G., Moond, V., Dahiya, D. S., Vora, R. S., & Dbouk, N. (2024) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16645 · Cited by 20
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Scheibner, A. C., Betthauser, K. D., Bewley, A. F., Juang, P., Lizza, B. D., Micek, S. T., & Lyons, P. G. (2022) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.2683 · Cited by 20
Huber, M., Schober, P. R., Petersen, S., & Luedi, M. M. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02156-w · Cited by 27
The applications of machine learning in computational thinking assessments: a scoping review
Tan, B., Jin, H.-Y., & Cutumisu, M. (2023) · Computer Science Education · doi:10.1080/08993408.2023.2245687 · Cited by 27
Ntaios, G., Sagris, D., Kallipolitis, A., Karagkiozi, E., Korompoki, E., Manios, E. D., Plagianakos, V., Vemmos, K. N., & Maglogiannis, I. G. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.106018 · Cited by 19
Hossain, M. Z., Daskalaki, E., Brüstle, A., Desborough, J., Lueck, C. J., & Suominen, H. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01985-5 · Cited by 19
Vicnesh, J., Chen, S., Oh, S. L., Acharya, U. R., & Chowbay, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106548 · Cited by 19
Prasad, N., Chien, I., Regan, T. W., Enrique, Á., Palacios, J. E., Keegan, D., Munir, U., Tanno, R., Richardson, H., Nori, A., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0272685 · Cited by 25
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King, A., Fowler, G., Macefield, R. C., Walker, H., Thomas, C., Markar, S. R., Higgins, E., Blazeby, J. M., & Blencowe, N. (2025) · BJS Open · doi:10.1093/bjsopen/zraf073 · Cited by 22
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Megna, R., Petretta, M., Assante, R., Zampella, E., Nappi, C., Gaudieri, V., Mannarino, T., D’Antonio, A., Green, R., Cantoni, V., et al. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/3551756 · Cited by 19
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Metadata
- Authors
- Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P.
- Affiliations
- American Heart Association (US)
- Texas A&M University (US)
- Brigham and Women's Hospital (US)
- Duke University (US)
- University of Colorado Anschutz (US)
- Venue
- Circulation: Cardiovascular Quality and Outcomes
- Published
- 2020-10
- Volume
- 13
- Issue
- 10
- Pages
- e006556-e006556
- ISSN
- 1941-7705 (ISSN-L)
- 1941-7713
- PMC
- PMC8320533
- OpenAlex
- W3094171951
- Cited by
- 268
- References
- 34
- Citations by year
- 2026: 39 · 2025: 43 · 2024: 72 · 2023: 46 · 2022: 43 · 2021: 24 · 2020: 1
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Circulation Cardiovascular Quality and Outcomes (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K08HL125725, L30HL110124)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- machine learning, reporting guidelines, clinical research, reproducibility, transparency, peer review, manuscript template, model validation, cardiovascular research
- MeSH terms
- Data Accuracy, Machine Learning, Data Interpretation, Statistical, Humans, Periodicals as Topic, Research Design, Editorial Policies, Biomedical Research
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.
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