Artificial Intelligence in Healthcare: A Narrative Review of Recent Clinical Applications, Implementation Strategies, and Challenges

Abstract

Clinical documentation demands are increasingly eroding clinician time and morale. Large language models (LLMs) are emerging as practical allies, drafting notes in real-time and laying the groundwork for decision support. This narrative review examines both recent clinical applications of AI across healthcare domains and leadership strategies for implementing these technologies in hospitals and ambulatory networks. We conducted a narrative review of recent literature and high-quality practice reports published, focusing on leadership strategies for implementing LLMs in hospitals and ambulatory networks. Evidence shows that when executives establish multidisciplinary AI committees, run quickly iterated pilots, and embed continuous bias and safety audits, LLM deployments improve workflow efficiency and clinician satisfaction without compromising quality. Effective programs pair clear vendor scorecards with transparent communication to staff and patients and align metrics with broader equity goals. Recent regulatory frameworks in North America and Europe reinforce the need for life-cycle governance and performance monitoring. The review concludes with a leadership roadmap linking strategic vision to practical actions that sustain safe, equitable, and financially sound LLM integration.

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