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Hospital environments face escalating demands for proactive, multimodal risk management amid rising patient complexity and data volume. While neural-enabled artificial intelligence has advanced specialized clinical decision support, existing systems remain fragmented, lacking unified coordination across electronic health record ecosystems, predictive modules, and governance mechanisms. This conceptual systems article introduces the neural-enabled risk orchestration (NERO) framework. This novel architectural model orchestrates multiple neural intelligence components into a cohesive topology for hospital-wide risk mitigation. Grounded exclusively in theoretical, infrastructural, and architectural principles, NERO comprises five interdependent layers—multimodal neural perception, risk propagation and connectivity, central orchestration engine, adaptive synthesis and prioritization, and governance feedback with drift mitigation—linked through bidirectional temporal feedback loops. The model addresses core gaps in current clinical AI architectures by enabling dynamic weighting of risk signals, context-aware decision synthesis, and continuous recalibration without empirical performance claims. Theoretical integration with interoperability standards and workflow models ensures seamless integration into hospital operations, while robust governance manages neural drift and compliance. By synthesizing advances in clinical decision support pipelines, EHR intelligence ecosystems, and AI monitoring systems, NERO offers a foundational blueprint for scalable, human-centric neural-enabled risk platforms. This orchestration-centric approach theoretically reduces decision latency trade-offs and enhances adaptive risk intelligence across acute and critical care settings.

The integration of large language models (LLMs) into clinical healthcare systems represents a transformative shift in how data analytics, decision support, and operational infrastructure are conceptualized and deployed. This narrative review synthesizes recent advancements in LLMs within healthcare, focusing on their roles in enhancing clinical analytics, infrastructural frameworks, and oversight mechanisms while addressing inherent risk dynamics. Drawing from peer-reviewed literature, we examine how LLMs facilitate the processing of vast unstructured clinical data, such as electronic health records and patient narratives, to generate actionable insights that inform diagnostics, treatment planning, and resource allocation. Key infrastructural elements include scalable deployment pipelines that integrate LLMs with existing hospital information systems, enabling real-time analytics and predictive modeling without disrupting legacy workflows. Oversight is emphasized through regulatory frameworks that ensure ethical deployment, data privacy compliance, and bias mitigation, as LLMs amplify risks related to misinformation, algorithmic opacity, and equitable access in diverse clinical settings. Risk dynamics are explored in terms of model hallucinations, dependency on training data quality, and potential for exacerbating healthcare disparities if not properly governed. The review highlights systems-level analytics where LLMs contribute to closed-loop healthcare ecosystems, from data ingestion and inference to feedback-driven recalibration, fostering adaptive intelligence in clinical decision-making. For instance, LLMs have been adapted for tasks like text summarization, diagnostic reasoning, and patient communication, outperforming traditional methods in efficiency while requiring robust validation to maintain clinical fidelity. We underscore the need for interdisciplinary collaboration between clinicians, data scientists, and policymakers to harness LLMs' potential in optimizing healthcare delivery. By synthesizing cross-study evidence, this review proposes an original interpretive framework for LLM-enabled healthcare systems, structured around data-model-deployment-governance cycles, to guide future implementations. Ultimately, while LLMs promise enhanced analytics and infrastructural resilience, their clinical adoption demands vigilant oversight to balance innovation with patient safety and ethical integrity. This synthesis not only maps the current landscape but also identifies infrastructural gaps in scaling LLMs for equitable, high-stakes clinical environments, paving the way for more resilient healthcare analytics paradigms.