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Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models
Open accessAbstract
Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. Materials and Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 01, 2010 and December 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing 6 major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks, yet none have been deployed in real-world healthcare settings. Five studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Fifteen studies proposed strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling and reweighting. Discussion: This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models’ practical impact and fostering ethical AI that ensures fairness and equity in healthcare.
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“Thus, its responses can reflect the subjective leanings originated from its data and embedded in its training, poten tially skewing responses in subtle yet significant ways (Chen et al., 2024).”
From If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing (2025) · cites this paper as Chen et al., 2024
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“However, these reviews often overlook the importance of addressing bias across multiple levels of the development process.”
From Advancing Health Equity through Multi-Level Fairness in Health Informatics (2026) · cites this paper as [6]
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Metadata
- Authors
- Chen, F., Wang, L., Hong, J., Jiang, J., & Zhou, L.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- Wellesley High School , Wellesley, MA 02481, United States
- Corresponding author
- Chen, Feng
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-03-23
- Volume
- 31
- Issue
- 5
- Pages
- 1172-1183
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11031231
- OpenAlex
- W4393119757
- Cited by
- 178
- References
- 48
- Citations by year
- 2026: 72 · 2025: 91 · 2024: 15
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Library of Medicine (1R01LM014239)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- algorithmic bias, bias detection, bias mitigation, electronic health records, artificial intelligence in healthcare, health care disparities, fairness metrics, implicit bias, selection bias, confounding, measurement bias, temporal bias, resampling, reweighting, ethical AI
- MeSH terms
- Algorithms, Artificial Intelligence, Female, Humans, Models, Theoretical, Pregnancy, Bias, Selection Bias, Benchmarking, Electronic Health Records
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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