The myth of generalisability in clinical research and machine learning in health care
Open accessAbstract
An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside.
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Institution-specific modeling strategies are preferable to universal solutions.
“it supports calls for institution-specific modeling strategies over universal solutions [19, 20].”
From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [20]
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“The development of generalizable predictive models in healthcare, particularly for ICUs, is an active area of research [20].”
Influential
- Authors
- Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A.
- Affiliations
- Harvard University (US)
- NYU Langone Health (US)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Celi, Leo Anthony
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 2
- Issue
- 9
- Pages
- e489-e492
- ISSN
- 2589-7500 (ISSN-L)
- PMC
- PMC7444947
- OpenAlex
- W3080627676
- Cited by
- 467
- References
- 6
- Citations by year
- 2026: 88 · 2025: 78 · 2024: 81 · 2023: 81 · 2022: 82 · 2021: 48 · 2020: 9
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01eb017205-07)
- National Institute of Biomedical Imaging and Bioengineering (R01-EB017205)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- generalizability, clinical utility, clinical research
- MeSH terms
- Machine Learning, COVID-19, SARS-CoV-2, Delivery of Health Care, Humans, Biomedical Research
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