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Dr. AI Harnessing the capabilities of large language models


By Nicholas J. Lima, Julian Ricci, Eleanor Froula, Hanssen Li, MD, Hari Trivedi, MD, Zachary L. Bercu, MD, John T. Moon, MD, Judy Gichoya, MD


the components into coherent human- like text. Reinforcement Learning from Human Feedback is a process that allows LLMs to finetune their responses using human input. Over time, refining LLM responses using human feedback creates a model with a more accurate representation of human knowledge and speech.2


human-like text after training on vast amounts of internet data to learn patterns, structures and nuances of language.1


These models collect


arge language models (LLMs) are artificial intelligence models that use machine learning to both process and generate


data and break it down into smaller components, such as sentences, phrases and words. Using probability- based algorithms, the models combine


LLMs have a wide range of utility, including text generation, question answering, content summarization, content creation, tutoring and coding assistance. Their sophistication has been highlighted in articles touting its ability to pass high-stakes exams including law and medical boards examination.3


In medicine, and


specifically within interventional radiology, there are opportunities to adapt LLM applications to enhance clinical care. LLMs may provide solutions to some of IR’s obstacles, ultimately improving patient care and promoting innovation.


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