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Generative artificial intelligence (GenAI) has emerged as a transformative force in healthcare systems, enabling advanced analytics, personalized interventions, and streamlined governance frameworks. This narrative review synthesizes recent literature on GenAI’s integration into healthcare infrastructures, emphasizing systems governance, safety protocols, and accountability mechanisms. We explore how GenAI enhances clinical decision-making, data analytics, and closed-loop systems while addressing ethical, regulatory, and operational challenges.At the core of healthcare systems, GenAI facilitates intelligent analytics by generating synthetic data for training models, simulating patient outcomes, and optimizing resource allocation. Governance frameworks are critical for ensuring responsible deployment, with studies highlighting the need for institutional guidelines that mitigate risks such as bias amplification and data privacy breaches. Safety considerations encompass algorithmic transparency, error detection in generative outputs, and human oversight in clinical loops. Accountability extends to lifecycle management, from model development to post-deployment monitoring, as evidenced by global initiatives and regional models like those in the GCC.The review delineates the landscape of GenAI applications in healthcare analytics, including predictive modeling for chronic disease management and real-time decision support. We propose an original systems-level framing that integrates data ingestion, inference generation, intervention deployment, and feedback recalibration under governance umbrellas. This synthesis reveals gaps in current infrastructures, such as the lack of standardized AI guardians for information overload and the challenges of scaling enterprise AI.In examining intelligent clinical decision systems, we highlight architectures that fuse GenAI with electronic health records (EHRs) for closed-loop operations, where generative models inform adaptive interventions. Ethical considerations are woven throughout, advocating for principles adapted from military contexts to healthcare. The adoption of GenAI in US hospitals underscores its potential for inpatient summaries and chronic care, yet calls for regulatory oversight to align with Helsinki declarations.Ultimately, this review positions GenAI as a cornerstone for accountable healthcare systems, urging interdisciplinary collaboration to balance innovation with safety. By synthesizing governance models, safety protocols, and accountability structures, we provide a roadmap for sustainable integration, fostering equitable health outcomes in an AI-augmented era.

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.