Assessing the Feasibility of Large Language Models to Identify Top Research Priorities in Enhanced External Counterpulsation

Abstract

AbstractEnhanced External Counterpulsation (EECP), as a non-invasive, cost-effective, and efficient adjunctive circulatory technique, has found increasingly widespread applications in the cardiovascular field. Numerous basic research and clinical observations have extensively confirmed the significant advantages of EECP in promoting blood flow perfusion to vital organs such as the heart, brain, and kidneys. However, many potential mechanisms of EECP remain insufficiently validated, necessitating researchers to dedicate substantial time and effort to in-depth investigations. In this work, we attempted to use large language models (such as ChatGPT and Ernie Bot) to identify top research priorities in five key topics in the field of EECP: mechanisms, device improvements, cardiovascular applications, neurological applications, and other applications. After generating specific research priorities in each domain through language models, we invited a panel of nine experienced EECP experts to independently evaluate and score them based on four parameters: relevance, originality, clarity, and specificity. Notably, average and median scores for these evaluation parameters were high, indicating a strong endorsement from experts in the EECP field. Although further validation and refinement are required, this study preliminarily suggests that large language models like ChatGPT and Ernie Bot could serve as powerful tools for identifying and prioritizing research priorities in the EECP domain.