Large Language Models (LLMs) for Clinical Workflow Automation: A Comprehensive Review

The healthcare industry is increasingly exploring the transformative potential of Large Language Models (LLMs) to automate and optimize complex clinical workflows. These advanced AI systems, characterized by their remarkable capabilities in natural language understanding and generation, offer promising solutions to alleviate physician burnout, reduce medical errors, enhance operational efficiency, and enable greater focus on patient care. This review provides a comprehensive overview of the current applications of LLMs in clinical workflow automation, ranging from sophisticated clinical documentation and reporting to advanced clinical decision support systems (CDSS) and patient engagement tools. We delve into the foundational principles and core capabilities of LLMs relevant to healthcare, and systematically categorize their diverse applications. Furthermore, this article critically examines the significant challenges and ethical considerations associated with their deployment, including issues of accuracy, data privacy, algorithmic bias, user adoption, and regulatory hurdles. Evaluation metrics and emerging case studies are discussed to gauge the efficacy and impact of these technologies. Finally, we explore the future trajectory of LLMs in healthcare, emphasizing the collaborative paradigm between human clinicians and AI, and offer recommendations for researchers and practitioners aiming to harness their full potential responsibly

کلیدواژه ها:

Large Language Models ، LLMs ، Artificial Intelligence in Medicine ، Clinical Workflow Automation ، Clinical Decision Support Systems ، Natural Language Processing ، Digital Health ، Healthcare