Health Care Implications of Large Language Models like ChatGPT – A Panel Discussion (2024)-WEB03-2024-RNSA-ChatGPT-Webinar-Supplemental-Resource-Guide

WEB03-2024-RNSA-ChatGPT-Webinar-Supplemental-Resource-Guide

Pdf Summary

The supplemental resource guide provides strategies for incorporating large language models (LLMs) like ChatGPT into clinical radiology, emphasizing their potential for enhancing diagnostic and decision-making processes. Recommended readings discuss LLM applications and their benefits, primarily targeting practicing radiologists. <br /><br />The guide suggests using advanced LLMs like GPT-3.5 and GPT-4 as the foundational technology for developing specialized medical chatbots. These models can improve clinical operations by assisting healthcare professionals with imaging decisions, summarizing reports, making differential diagnoses, and suggesting treatments through natural language processing capabilities. <br /><br />For implementing these technologies effectively, the guide recommends regular updates to the chatbot's knowledge base to keep pace with the latest medical guidelines and research. Additionally, the chatbots should undergo rigorous validation and testing to ensure reliability and consistency with healthcare standards before deployment. <br /><br />A focus on addressing biases in training data by curating high-quality, authoritative medical data is stressed to maintain the chatbot's reliability and fairness. It is vital that these tools augment rather than replace the expertise and clinical judgment of radiologists.<br /><br />The guide also explores a customized chatbot—accGPT—that offers personalized imaging recommendations based on the American College of Radiology (ACR) guidelines, highlighting the potential for context-aware chatbots to enhance guideline adherence in radiology.<br /><br />Further, research showcased in the guide indicates GPT-4's competence in aligning imaging recommendations with expert decisions. It encourages integrating GPT-4 into hospital systems for automated decision support, pre-selecting protocol suggestions based on patient information. However, transparency in GPT-4's recommendations requires accountability measures, such as clear references and a thorough regulatory review for clinical tool adoption. <br /><br />Overall, the guide highlights the transformative potential of LLMs in radiology while emphasizing responsible implementation and validation.

Keywords

large language models

clinical radiology

ChatGPT

diagnostic enhancement

medical chatbots

GPT-4

imaging decisions

bias mitigation

American College of Radiology

automated decision support