ACCEL Lite: Getting the Most Out of ChatGPT and Other Large Language Models in Cardiology

Drafting messages, summarizing charts, managing appointments, and assessing eligibility are all tasks that large language models can streamline. However, integrating them safely and effectively—whether in small practices or large healthcare institutions—poses unique challenges.

In this interview, Drs. Dipti Itchhaporia and Marly van Assen explore key strategies and safeguards designed to reduce errors and enhance reliability in high-stakes environments like healthcare.

Related References:

  1. Getting the Most Out of ChatGPT and Other Large Language Models in Cardiology. Presented by Dr. Marly van Assen at the American Heart Association Annual Scientific Session (AHA24), Chicago, IL, November 16, 2024. 
  2. Williams MC, Weir-McCall JR, Baldassarre LA, et al. Artificial intelligence and machine learning for cardiovascular computed yomography (CCT): a white paper of the Society of Cardiovascular Computed Tomography (SCCT). J Cardiovasc Comput Tomogr 2024;18:519-32.
  3. Ballard DH, Antigua-Made A, Barre, et al. Impact of ChatGPT and Large Language Models on Radiology Education: Association of Academic Radiology-Radiology Research Alliance Task Force White Paper. Acad Radiol 2024;Nov 29:[ePub ahead of print].

Clinical Topics: Prevention

Keywords: ACCELLite, Cardiology, AHA24