The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
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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ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · cited 2×
“Our analyses were further enhanced by consolidating established bias risk assessment tools, such as ROBINS-I, 16 ROBINS-E, 17 and PROBAST, 18 and relevant review articles, such as Mehrabi et al 19 which categorized potential biases in AI applications.”
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · cited 1×
“8,9 Biases can emerge at various stages of the model development lifecycle, 10–12 underlining the importance of developing and implementing strategies for effective detection, mitigation, and evaluation of biases.”
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine · doi:10.7326/m18-1376 · Cited by 2,984 · cited 1×
“Our analyses were further enhanced by consolidating established bias risk assessment tools, such as ROBINS-I, 16 ROBINS-E, 17 and PROBAST, 18 and relevant review articles, such as Mehrabi et al 19 which categorized potential biases in AI applications.”
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · cited 1×
“46 On the other hand, AI models may exhibit biases, and if left unaddressed, they have the potential to exacerbate healthcare disparities.”
Implicit bias in healthcare professionals: a systematic review
FitzGerald, C., & Hurst, S. (2017) · BMC Medical Ethics · doi:10.1186/s12910-017-0179-8 · Cited by 2,556 · cited 1×
“22 It often stems from preexisting bias within the data (such as stereotypes and flawed case assumptions) which may occur in the data collection and data preprocessing steps.”
Scalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242
Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395 · cited 1×
“48,49 In this systematic review, we have comprehensively identified and scrutinized potential biases in AI models developed from EHR data, revealing their potential influence on healthcare disparities.”
Do no harm: a roadmap for responsible machine learning for health care
Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0548-6 · Cited by 1,266 · cited 1×
“13 While a few scoping reviews have been conducted to understand bias in broader medical AI, 14,15 a focused review on AI models derived from EHR data is notably absent.”
Research works printed but not linked: 1
- Johnson, 2000, Sampling bias and other methodological threats to the validity of health survey research, Int J Stress Manag, 7, 247, 10.1023/A:1009589812697 · DOI
Cited by
Sorted by citations
Saeidnia, H. R., Fotami, S. G. H., Lund, B. D., & Ghiasi, N. (2024) · Social Sciences · doi:10.3390/socsci13070381 · Cited by 227
“Future research should aim to overcome these limitations to provide a more comprehensive understanding of ethical considerations in AI-based mental health interventions and to inform responsible practice and policy development.”
Bouguettaya, A., Stuart, E. M., & Aboujaoude, E. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01746-4 · Cited by 60
Transparency of medical artificial intelligence systems
Kim, C., Gadgil, S. U., & Lee, S. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00363-w · Cited by 60
“Detailed documentation allows developers to mitigate potential biases during model training using computational techniques, even if biases are discovered after data collection 17,66,67 .”
Jamshidi, S., Nikanjam, A., Nafi, K. W., Khomh, F., & Rasta, R. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101531 · Cited by 55
Mei, P., Brewis, D. N., Nwaiwu, F., Sumanathilaka, D., Alva-Manchego, F., & Demaree-Cotton, J. (2025) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2025.100140 · Cited by 50
“Thus, its responses can reflect the subjective leanings originated from its data and embedded in its training, potentially skewing responses in subtle yet significant ways (Chen et al., 2024).”
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
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