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The integration of artificial intelligence into healthcare systems has transitioned from isolated diagnostic tools to comprehensive workflow automation platforms that fundamentally reshape clinical operations, decision cycles, and accountability frameworks. This narrative review synthesizes studies that examine how AI-driven task modeling, human–AI interaction dynamics, and evolving accountability structures collectively enable scalable, safe, and ethically grounded automation across healthcare analytics and delivery infrastructures. Rather than cataloging isolated applications, the analysis adopts an original systems-level lens that organizes the literature into four interdependent layers—data orchestration, model orchestration, deployment orchestration, and governance orchestration—revealing recurring patterns of closed-loop intelligence that link real-time data ingestion to automated intervention and continuous recalibration. Task modeling emerges as the foundational mechanism through which heterogeneous clinical workflows are decomposed into machine-executable primitives while preserving human oversight at critical decision nodes. Multiple integrative reviews demonstrate that well-designed task ontologies reduce cognitive burden on clinicians by 30%–50% in high-volume settings such as nursing documentation, pathology slide triage, and echocardiographic measurement, yet success critically depends on explicit representation of human factors, including workload, trust calibration, and exception-handling protocols. Human factors literature further highlights the bidirectional influence between automation and clinician performance. While AI scribes and large language model-assisted note generation improve throughput, they simultaneously introduce new forms of automation bias and alert fatigue that must be mitigated through adaptive interface design and real-time transparency mechanisms. Accountability structures constitute the least mature yet most decisive layer of AI-enabled healthcare automation. Governance models that embed continuous human–AI shared liability, audit trails for every automated decision, and dynamic recalibration triggers are shown to be essential for regulatory acceptance and clinical adoption. Studies of real-world deployments in pathology foundation models and closed-loop infection prevention systems illustrate that accountability is not an afterthought but an infrastructural requirement: without traceable lineage from raw data through model inference to clinical action and feedback, organizations cannot fulfill medico-legal or ethical obligations. This review contributes an original integrative framework—the clinical intelligence loop—that formalizes the end-to-end automation architecture as a continuous cycle of data ingestion, task-modeled inference, human-augmented decision fusion, intervention execution, outcome monitoring, and governance-driven recalibration. Cross-study synthesis reveals that systems achieving sustained performance do so by maintaining tight coupling across all five stages rather than optimizing any single component in isolation. The analysis underscores that workflow automation in healthcare AI succeeds only when task modeling is human-centered, human factors are explicitly engineered into the loop, and accountability is infrastructural rather than retrofitted. These insights provide both theoretical scaffolding and practical guidance for health-system leaders, regulators, and technology developers seeking to scale responsible AI automation beyond pilot projects.

The rapid deployment of artificial intelligence in healthcare analytics has outpaced the development of governance structures needed to monitor, audit, and ensure the continuing safety and equity of these systems. This gap is especially consequential when models influence clinical prioritization, operational resource allocation, or population health management. This systematic review critically examined literature on artificial intelligence governance for healthcare analytics. The review focused on model monitoring, bias auditing, data drift detection, accountability structures, and regulatory readiness. A PRISMA 2020–aligned search strategy was applied across PubMed, Scopus, IEEE Xplore, and Web of Science. Dual screening, structured data extraction, and narrative synthesis were used to evaluate frameworks, tools, implementation practices, and reported barriers. The review found numerous frameworks and methods addressing individual governance tasks, including performance monitoring, calibration surveillance, fairness assessment, and documentation. However, comprehensive governance systems that integrate technical monitoring with organizational accountability in live healthcare environments remained uncommon. Artificial intelligence governance in healthcare analytics remains fragmented and inconsistently operationalized. Monitoring and drift detection were comparatively more mature than accountability structures, bias audit workflows, and regulatory readiness practices.