Challenges to the Reproducibility of Machine Learning Models in Health Care
Open accessAbstract
This Viewpoint reviews how machine learning models are developed and trained, discusses the challenges of reproducing medical machine learning model accuracy, and proposes practices and standards to improve the models’ reproducibility and replicability.
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Suboptimal quality of clinical data often limits the performance of machine learning algorithms.
“Suboptimal quality of clinical data often limits the performance of ML algorithms (6,7).”
From Mitigating Bias in Radiology Machine Learning: 1. Data Handling (2022) · cites this paper as [7]
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Cites
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Research works printed but not linked: 2
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Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
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Self-citationSecond opinion needed: Communicating uncertainty in medical machine learning
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Reproducibility in machine learning for health research: Still a ways to go
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Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K. (2024) · New England Journal of Medicine · doi:10.1056/nejmra2214183 · Cited by 149 · see citing works
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The Diagnostic and Triage Accuracy of the GPT-3 Artificial Intelligence Model
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Towards reproducible radiomics research: introduction of a database for radiomics studies
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- Authors
- Beam, A. L., Manrai, A. K., & Ghassemi, M.
- Affiliations
- Harvard University (US)
- Boston Children's Hospital (US)
- University of Toronto (CA)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2020-01-28
- Volume
- 323
- Issue
- 4
- Pages
- 305
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC7335677
- OpenAlex
- W2997681568
- Cited by
- 366
- References
- 7
- Citations by year
- 2026: 63 · 2025: 67 · 2024: 52 · 2023: 51 · 2022: 53 · 2021: 46 · 2020: 34
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K01HL138259, K01HL141771)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- machine learning reproducibility, model accuracy, replicability, health care
- MeSH terms
- Machine Learning, Delivery of Health Care, Humans, Reproducibility of Results, Biomedical Research
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