mlsanityData to 7 Oct 2026

Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models

arXiv 2310.19917 ↗First posted 30 Oct 2023cs.AI cs.CY cs.LGcited by 9

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Abstract (arXiv)

Objectives: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. Yet, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to detect and mitigate diverse forms of bias in AI models developed using EHR data. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, analyzing articles from PubMed, Web of Science, and IEEE published between January 1, 2010, and Dec 17, 2023. The review identified key biases, outlined strategies for detecting and mitigating bias throughout the AI model development process, and analyzed metrics for bias assessment. Results: Of the 450 articles retrieved, 20 met our criteria, revealing six major bias types: algorithmic, confounding, implicit, measurement, selection, and temporal. The AI models were primarily developed for predictive tasks in healthcare settings. Four studies concentrated on the detection of implicit and algorithmic biases employing fairness metrics like statistical parity, equal opportunity, and predictive equity. Sixty proposed various strategies for mitigating biases, especially targeting implicit and selection biases. These strategies, evaluated through both performance (e.g., accuracy, AUROC) and fairness metrics, predominantly involved data collection and preprocessing techniques like resampling, reweighting, and transformation. Discussion: This review highlights the varied and evolving nature of strategies to address bias in EHR-based AI models, emphasizing the urgent needs for the establishment of standardized, generalizable, and interpretable methodologies to foster the creation of ethical AI systems that promote fairness and equity in healthcare.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 69 references that are in the map. First posted on arXiv 30 Oct 2023.

Builds on

19 of 69 references.

References · 19

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2014Certifying and removing disparate impactcited by 715
  4. 2020Fairness in Machine Learning: A Surveycited by 369
  5. 2018Scalable and accurate deep learning for electronic health recordscited by 275
  6. 2015Censoring Representations with an Adversarycited by 191
  7. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  8. 2017A Convex Framework for Fair Regressioncited by 147
  9. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  10. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  11. 2017Calibrated Fairness in Banditscited by 42
  12. 2020FAE: A Fairness-Aware Ensemble Frameworkcited by 34
7 more
  1. 2019Improved Adversarial Learning for Fair Classificationcited by 25
  2. 2018Enhancing the Accuracy and Fairness of Human Decision Makingcited by 13
  3. 2023Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustmentcited by 5
  4. 2023A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factorscited by 4
  5. 2020An Adversarial Domain Separation Framework for Septic Shock Early Prediction Across EHR Systemscited by 3
  6. 2023Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomescited by 3
  7. 2023A survey on bias in machine learning researchcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 3.

9 papers.

  1. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 11
  2. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  3. 2025Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Reviewcited by 2
  4. 2025The Multimodal Paradox: How Added and Missing Modalities Shape Bias and Performance in Multimodal AIcited by 1
  5. 2024Fair Streaming Feature Selectioncited by 0
  6. 2025Adapting Large Language Models to Mitigate Skin Tone Biases in Clinical Dermatology Tasks: A Mixed-Methods Studycited by 0
  7. 2026Generative Artificial Intelligence for Literature Reviewscited by 0
  8. 2026From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Healthcited by 0
  9. 2026Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disordercited by 0