Large language models in critical care - Laurens A. Biesheuvel; Jessica D. Workum; Merijn Reuland; Michel E. van Genderen; Patrick Thoral; Dave Dongelmans; Paul Elbers (Journal of Intensive Medicine)
The advent of chat generative pre-trained transformer (ChatGPT) and large language models (LLMs) has revolutionized natural language processing (NLP). These models possess unprecedented capabilities in understanding and generating human-like language. This breakthrough holds significant promise for critical care medicine, where unstructured data and complex clinical information are abundant. Key applications of LLMs in this field include administrative support through automated documentation and patient chart summarization; clinical decision support by assisting in diagnostics and treatment planning; personalized communication to enhance patient and family understanding; and improving data quality by extracting insights from unstructured clinical notes. Despite these opportunities, challenges such as the risk of generating inaccurate or biased information “hallucinations”, ethical considerations, and the need for clinician artificial intelligence (AI) literacy must be addressed. Integrating LLMs with traditional machine learning models – an approach known as Hybrid AI – combines the strengths of both technologies while mitigating their limitations. Careful implementation, regulatory compliance, and ongoing validation are essential to ensure that LLMs enhance patient care rather than hinder it. LLMs have the potential to transform critical care practices, but integrating them requires caution. Responsible use and thorough clinician training are crucial to fully realize their benefits.