Vol. 2, Issue 1, Part A (2025)

Large Language Models (LLMs) are reshaping the landscape of artificial intelligence in healthcare, offering novel possibilities for automating clinical documentation, enhancing patient communication, and supporting decision-making processes. This study comprehensively reviews 45 peer-reviewed publications and regulatory documents to assess both the opportunities and challenges associated with the integration of LLMs into healthcare systems. The analysis identified documentation summarization, diagnostic assistance, and medical education as the most prominent application domains. Statistical examination revealed a significant association between evaluation rigor and reported performance benefits, with benchmark-based studies showing higher positive outcomes compared to real-world or randomized trials, suggesting a persistent “evaluation illusion.” Commonly reported risks included hallucination, data bias, privacy concerns, and regulatory uncertainty, while mitigation strategies such as human-in-the-loop supervision, prompt guardrails, domain adaptation, and governance frameworks emerged as best practices. The findings affirm that the responsible use of LLMs in healthcare depends not solely on model capability but on the robustness of evaluation, ethical oversight, and compliance with evolving regulatory standards. The study concludes that, when deployed in low-risk, well-structured, and human-supervised contexts, LLMs can improve clinical efficiency, accessibility, and communication. However, their transition from controlled settings to real-world practice demands rigorous validation, continuous monitoring, and policy-level coordination. The paper offers practical recommendations emphasizing interdisciplinary governance, AI literacy for clinicians, and domain-specific fine-tuning to minimize risk and maximize utility. Overall, this research highlights that LLMs possess transformative potential when aligned with human expertise, transparent governance, and evidence-driven evaluation, paving the way toward safer, ethically grounded, and effective digital healthcare ecosystems.