In medicine, how do we machine learn anything real?
Open accessAbstract
Machine learning has traditionally operated in a space where data and labels are assumed to be anchored in objective truths. Unfortunately, much evidence suggests that the "embodied" data acquired from and about human bodies does not create systems that function as desired. The complexity of health care data can be linked to a long history of discrimination, and research in this space forbids naive applications. To improve health care, machine learning models must strive to recognize, reduce, or remove such biases from the start. We aim to enumerate many examples to demonstrate the depth and breadth of biases that exist and that have been present throughout the history of medicine. We hope that outrage over algorithms automating biases will lead to changes in the underlying practices that generated such data, leading to reduced health disparities.
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These findings identify a probable training data bias that predisposes models to provide confident answers in most scenarios, even when a more cautious response is warranted.
“These findings reinforce previous research recommendations on mitigating healthcare data biases in machine learning30, identifying a probable training data bias that predisposes models to provide confident answers in most scenarios, even when a more cautious response is warranted.”
From Large Language Models lack essential metacognition for reliable medical reasoning (2025) · cites this paper as [30]
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- Authors
- Ghassemi, M., & Nsoesie, E. O.
- Affiliations
- Canadian Institute for Advanced Research (CA)
- Boston University (US)
- Corresponding author
- Ghassemi, Marzyeh
- Venue
- Patterns
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 3
- Issue
- 1
- Pages
- 100392
- ISSN
- 2666-3899 (ISSN-L)
- PMC
- PMC8767288
- OpenAlex
- W4210508008
- Cited by
- 37
- References
- 49
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 9 · 2023: 11 · 2022: 5
- FWCI
- 1.43
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Patterns (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- algorithmic bias, health data, health disparities, history of medicine, health care discrimination, machine learning bias, health data collection, algorithmic fairness
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