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Artificial intelligence models are increasingly used to support healthcare decision-making, resource allocation, clinical triage, population health management, and quality improvement. Without explicit equity assessment, these tools may reproduce, obscure, or scale existing disparities across racial, ethnic, sex, gender, socioeconomic, and geographic groups. This systematic review examined artificial intelligence approaches for healthcare equity analytics from 2017 to 2024. The review focused on bias detection, fairness-aware prediction, disparity monitoring, and algorithmic accountability in health systems. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for peer-reviewed English-language studies published between 2017 and 2024. Eligible records were screened by two reviewers, and findings were synthesised narratively by equity task, method type, clinical application, fairness metric, and implementation context. The evidence base showed increasing attention to bias detection and fairness-aware algorithm design, especially in clinical risk prediction, diagnostic imaging, and population health management. Disparity monitoring systems and accountability structures were less frequently evaluated in deployed health system environments and were commonly described as governance recommendations, audit frameworks, or pilot-stage approaches. The technical foundations for equitable artificial intelligence in healthcare are advancing, but translation into operational health system practice remains limited. The strongest evidence concerns bias measurement, whereas evidence that fairness interventions reduce real-world health disparities remains nascent.

Clinical triage algorithms increasingly influence access to emergency care, specialty referral, admission, and follow-up. As patient populations and clinical practice patterns change, these systems can silently drift toward biased performance. Current fairness assessments are often retrospective, episodic, and disconnected from operational triage workflows. They may identify inequity after harm has already accumulated rather than detecting emerging bias as it develops. This article proposes an explainable AI model for continuous monitoring of algorithmic bias in clinical triage systems. The model focuses on demographic drift, outcome disparities, prediction confidence, and referral decision patterns as complementary bias signals. The proposed model uses operational triage logs, demographic distributions, prediction outputs, outcome indicators, and referral decisions to generate a conceptual fairness risk signal. SHAP-based explanation modules decompose the signal into interpretable contributors for clinical governance teams. Conceptually, the model would detect divergence in referral patterns across demographic groups, identify whether the divergence coincides with demographic drift, flag subgroup-specific prediction confidence concerns, and explain the likely drivers of the alert. The output would support timely review rather than automated punitive action. The model could transform algorithmic fairness from a periodic retrospective report into a continuous, transparent, and operationally actionable surveillance system. It is designed as a governance-oriented framework rather than an experimental performance claim.