Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals

MSC 804 5 - 0020 - 10 St. Louis, MO 63110 Financial Support: P30 - AR073752 Conflict of interest: No conflicts declared. Page 2 of 13 Roberson 2025 - LLMs in medicine Abstract The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacen t research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models ( LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions . However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclo sure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technol ogies while avoiding the dangers in each context. Page 3 of 13 Roberson 2025 - LLMs in medicine Abbreviations AI: Artificial intelligence CSP: Constrained solution problems EHR: Electronic health records GPT: Generative pre - trained transformer LLM: Large language model ML: Machine learning RAG: Retrieval - augmented generation Page 4 of 13 Roberson 2025 - LLMs in medicine What is a large language model? Large language models are one of the most hyped artificial intelligence technologies of the past few years. Buzz terms associated with them include artificial intelligence, machine learning, a nd deep learning.