Battle of the (Chat)Bots: Comparing Large Language Models to Practice Guidelines for Transfusion-Associated Graft-Versus-Host Disease Prevention

Abstract

Published guidelines and clinical practices vary when defining indications for irradiation of blood components for the prevention of transfusion-associated graft-versus-host disease (TA-GVHD). This study assessed irradiation indication lists generated by multiple artificial intelligence (AI) programs, or chatbots, and compared them to 2020 British Society for Haematology (BSH) practice guidelines. Four chatbots (ChatGPT-3.5, ChatGPT-4, Bard, and Bing Chat) were prompted to list the indications for irradiation to prevent TA-GVHD. Responses were graded for concordance with BSH guidelines. Chatbot response length, discrepancies, and omissions were noted. Chatbot responses differed, but all were relevant, short in length, generally more concordant than discordant with BSH guidelines, and roughly complete. They lacked several indications listed in BSH guidelines and notably differed in their irradiation eligibility criteria for fetuses and neonates. The chatbots variably listed erroneous indications for TA-GVHD prevention, such as patients receiving blood from a donor who is of a different race or ethnicity. This study demonstrates the potential use of generative AI for transfusion medicine and hematology topics but underscores the risk of chatbot medical misinformation. Further study of risk factors for TA-GVHD, as well as the applications of chatbots in transfusion medicine and hematology, is warranted.

Keywords: Artificial intelligence; Blood transfusion; Medical ethics; Transfusion medicine.