Transforming Healthcare: Opportunities And Challenges In Harnessing Large Language Models

Abstract

The integration of large language models (LLMs) in healthcare represents a significant advancement in medical technology, offering solutions to increasingly complex challenges in clinical practice. This comprehensive review examines the current state, opportunities, and limitations of LLM deployment in medical domains. We analyze how these models address critical issues such as clinical data overload, administrative inefficiencies, and medical education while accelerating drug development processes. Drawing on recent benchmarks, including MEDEC's evaluation of error detection and correction in clinical notes, we demonstrate that advanced LLMs approach near-expert performance in specific medical tasks. However, significant challenges persist, including the risk of hallucination, lack of transparency, liability concerns, data privacy issues, and potential biases. Our analysis reveals that while LLMs show remarkable promise in transforming healthcare delivery, their implementation requires careful validation and ethical oversight. We propose a balanced approach combining rigorous benchmarking, explainable AI methodologies, and comprehensive ethical frameworks, emphasizing the importance of maintaining human oversight in clinical decision-making. This review concludes that the optimal path forward lies in human-AI collaboration, where LLMs augment rather than replace clinical expertise, ensuring both technological advancement and patient safety. These findings have important implications for healthcare providers, medical educators, and policymakers as they navigate the integration of AI technologies in medical practice.