Steps to avoid overuse and misuse of machine learning in clinical research
Open accessAbstract
Machine learning algorithms are a powerful tool in healthcare, but sometimes perform no better than traditional statistical techniques. Steps should be taken to ensure that algorithms are not overused or misused, in order to provide genuine benefit for patients.
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Not all ML and AI models outperform traditional models in healthcare and public health areas.
“it is crucial to recognize that not all ML and AI models outperform traditional models in healthcare and public health areas [64, 65].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [64]
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Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works
The cancer genome atlas pan-cancer analysis project
The Cancer Genome Atlas Research Network, Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., & Stuart, J. M. (2013) · Nature Genetics · doi:10.1038/ng.2764 · Cited by 9,092 · see citing works
Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · doi:10.1101/2020.03.24.20041020 · Cited by 3,070 · see citing works
Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.004 · Cited by 2,119 · see citing works
Reproducible brain-wide association studies require thousands of individuals
Marek, S. A., Tervo‐Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., Donohue, M. R., Foran, W., Miller, R. L., Hendrickson, T. J., et al. (2022) · Nature · doi:10.1038/s41586-022-04492-9 · Cited by 2,110 · see citing works
Comparing different supervised machine learning algorithms for disease prediction
Uddin, S., Khan, A. M., Hossain, M. E., & Moni, M. A. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-1004-8 · Cited by 1,687 · see citing works
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · see citing works
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., & Padoy, N. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2593957 · Cited by 1,116 · see citing works
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01772-9 · Cited by 1,039 · see citing works
Self-citationAI can be sexist and racist — it’s time to make it fair
Zou, J., & Schiebinger, L. (2018) · Nature · doi:10.1038/d41586-018-05707-8 · Cited by 765 · see citing works
AI for radiographic COVID-19 detection selects shortcuts over signal
DeGrave, A. J., Janizek, J. D., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00338-7 · Cited by 631 · see citing works
Improving the accuracy of medical diagnosis with causal machine learning
Richens, J. G., Lee, C. M., & Johri, S. (2020) · Nature Communications · doi:10.1038/s41467-020-17419-7 · Cited by 598 · see citing works
The myth of generalisability in clinical research and machine learning in health care
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30186-2 · Cited by 467 · see citing works
Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ · doi:10.1136/bmj.n2281 · Cited by 415 · see citing works
Gravesteijn, B. Y., Nieboer, D., Ercole, A., Lingsma, H. F., Nelson, D., CALSTER, B. V., Steyerberg, E. W., Åkerlund, C. A. I., Amrein, K., Anđelić, N., et al. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.03.005 · Cited by 184 · see citing works
Self-citationShin, S., Austin, P. C., Ross, H. J., Abdel-Qadir, H. M., Freitas, C., TOMLINSON, G. A., Chicco, D., Mahendiran, M., Lawler, P. R., Billia, F., et al. (2020) · ESC Heart Failure · doi:10.1002/ehf2.13073 · Cited by 184 · see citing works
Stulberg, J. J., & Haut, E. R. (2018) · JAMA Surgery · doi:10.1001/jamasurg.2018.0542 · Cited by 144 · see citing works
Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
Shashikumar, S. P., Wardi, G., Malhotra, A., & Nemati, S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00504-6 · Cited by 130 · see citing works
Welcoming new guidelines for AI clinical research
Topol, E. J. (2020) · Nature Medicine · doi:10.1038/s41591-020-1042-x · Cited by 116 · see citing works
Cho, S. M., Austin, P. C., Ross, H. J., Abdel-Qadir, H. M., Chicco, D., TOMLINSON, G. A., Taheri, C., Foroutan, F., Lawler, P. R., Billia, F., et al. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.02.020 · Cited by 64 · see citing works
Volovici, V., Steyerberg, E. W., Cnossen, M. C., Haitsma, I., Dirven, C. M. F., Maas, A. I. R., & Lingsma, H. F. (2019) · Journal of Neurotrauma · doi:10.1089/neu.2019.6474 · Cited by 59 · see citing works
Self-citationTenets of Good Practice in Regression Analysis. A Brief Tutorial
Pisică, D., Dammers, R., Boersma, E. H., & Volovici, V. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.02.112 · Cited by 43 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 7
- Wynants, L. et al. Br. Med. J. 369, m1328 (2020).
- Abràmoff, M. D. et al. npj Digit. Med. 1, 39 (2018).
- Uddin, S. et al. BMC Med. Inform. Decis. Mak. 19, 281 (2019).
- Shashikumar, S. P. et al. npj Digit. Med. 4, 134 (2021).
- Richens, J. G. et al. Nat. Commun. 11, 3923 (2020).
- Andaur Navarro, C. L. et al. Br. Med. J. 375, n2281 (2021).
- Twinanda, A. P. et al. IEEE Trans. Med. Imaging 36, 86-97 (2017).
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Yang, R., Tan, T. F., Lu, W., Thirunavukarasu, A. J., Ting, D. S. W., & Liu, N. (2023) · Health Care Science · doi:10.1002/hcs2.61 · Cited by 366 · see citing works
Self-citationGil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2024) · Political Communication · doi:10.1080/10584609.2023.2290497 · Cited by 198 · see citing works
Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis
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 · see citing works
Sokol, K., Fackler, J., & Vogt, J. E. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01725-9 · Cited by 105 · see citing works
A translational perspective towards clinical AI fairness
Liu, M., Ning, Y., Teixayavong, S., Mertens, M., Xu, J., Ting, D. S. W., Cheng, L. T.-E., Ong, J. C. L., Teo, Z. L., Tan, T. F., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00918-4 · Cited by 82 · see citing works
Self-citationArina, 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 · see citing works
Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works
- Authors
- Volovici, V., Syn, N. L., Ercole, A., Zhao, J. J., & Liu, N.
- Affiliations
- Erasmus MC (NL)
- National University of Singapore (SG)
- University of Cambridge (GB)
- Corresponding author
- Volovici, Victor
- Venue
- Nature Medicine
- Published
- 2022-09-12
- Volume
- 28
- Issue
- 10
- Pages
- 1996-1999
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4295472519
- Cited by
- 110
- References
- 23
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 34 · 2023: 38 · 2022: 3 · 2021: 1
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion, other-oa)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- Duke-NUS Medical School
- National University of Singapore
- National University Health System
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning, clinical research, algorithmic bias, clinical decision support, patient benefit
- MeSH terms
- Machine Learning
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