Potential Multidisciplinary Use of Large Language Models for Addressing Queries in Cardio‐Oncology
Pengfei Li, Xuejuan Zhang, Erjia Zhu, Shijun Yu, Bin Sheng, Yih Chung Tham, Tien Yin Wong, Hongwei Ji
Abstract
n the crossroads of digital health and education, large language models (LLMs) emerge as tools with great potential. Trained on expansive textual data sets, these state-of-the-art artificial intelligence models can generate multidisciplinary content, answer intricate queries, and accelerate information delivery. articularly in the field of cardio-oncology, which combines cardiac and oncological expertise, LLMs have the potential to provide valuable insights to specialists like cardiologists and oncologists. 2 This is useful in situations in which standard guidelines are not immediately available or when there is a need to combine a vast amount of interdisciplinary information. However, the performances of LLMs in this context remains largely unknown. This study aims to benchmark these state-of-the-art artificial intelligence models in their ability to handle the interdisciplinary queries inherent in cardio-oncology, where integrative insights from cardiology and oncology are crucial.