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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.