  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.
