The Application of Large Language Models for Radiologic Decision Making.

Background And Purpose: Large language models (LLMs) have seen explosive growth, but their potential role in medical applications remains underexplored. Our study investigates the capability of LLMs to predict the most appropriate imaging study for specific clinical presentations in various subspecialty areas in radiology.

Methods And Materials: Chat Generative Pretrained Transformer (ChatGPT), by OpenAI and Glass AI by Glass Health were tested on 1,075 clinical scenarios from 11 ACR expert panels to determine the most appropriate imaging study, benchmarked against the ACR Appropriateness Criteria.

Use of Large Language Models to Predict Neuroimaging.

Purpose: Large language models (LLMs) have demonstrated a level of competency within the medical field. The aim of this study was to explore the ability of LLMs to predict the best neuroradiologic imaging modality given specific clinical presentations. In addition, the authors seek to determine if LLMs can outperform an experienced neuroradiologist in this regard.