The application of artificial intelligence (AI) in healthcare has tremendous potential for improving diagnostic precision and optimizing treatment and patient care. However, increasing dependence on such tools brings up urgent questions regarding the amplification of existing biases, which may detract from their ability to improve fair clinical decision-making. Adversarial debiasing, a method that utilizes fairness measures by contrasting a core predictive model with an adversarial network to reduce the influence of sensitive features, has emerged as an effective way of mitigating bias in AI systems. This review combines findings from 25 studies on several areas, encompassing the technical elements of adversarial learning and its practical applications in healthcare. The review offers extensive data and thoroughly assesses technological, ethical, and practical issues. This study reveals that adversarial debiasing improves fairness indicators and presents significant trade-offs, including reduced sensitivity and interpretability. We conclude with recommendations for future research avenues, encompassing prospective multicenter trials, adaptive training methodologies, hybrid debiasing strategies, and formulating standardized regulatory frameworks.
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