Abstract
ObjectiveTo provide a comprehensive overview of the current use of large language models in clinical medicine and surgery, with emphasis on model characteristics, clinical applications, and readiness for adoption.MethodsA scoping review of studies on the use of large language models in clinical medicine and surgery was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-scoping review and JBI methodology (protocol registration: 10.37766/inplasy2025.3.0102). A comprehensive search of EMBASE, PubMed, CINAHL, and IEEE Xplore identified 3313 articles published between 2018 and 2023. After screening of articles and full-text review, 156 studies were included. Data were extracted for study type, sample size, clinical specialty, model architecture, training methods, application purpose, and performance metrics. Descriptive analyses were performed.ResultsMost studies were proof-of-concept studies (55.8%) or clinical trials (21.2%), with a steady rise in publications since 2022. Large language models were most frequently used for data extraction (69.9%), followed by clinical recommendations (11.5%), report generation (9.0%), and patient-facing chatbots (7.1%). Proprietary models were used in 57.7% of the studies, whereas 39.7% used open-source models. ChatGPT-3.5, ChatGPT-4, and Bidirectional Encoder Representations from Transformers (BERT) were the most commonly reported models. Only 25.0% of the studies reported models as ready for clinical use, whereas 67.9% stated that the models required further validation. F-score (30.8%) and area under the curve (15.4%) were the most common performance metrics; 10.9% of the studies used expert opinion for validation.ConclusionsLarge language models are increasingly being used in clinical medicine. Although most applications focus on data extraction and summarization, emerging studies are beginning to explore higher-level tasks such as clinical decision-making and multidisciplinary simulation. Significant heterogeneity continues to exist in model architecture, evaluation methods, and reporting standards. Further standardization is needed to develop transparent evaluation frameworks and ensure safe, reliable integration of large language models into complex clinical workflows.