Use of artificial intelligence in transfusion medicine practice, education and research: A mixed methodology study

Abstract

BACKGROUND AND OBJECTIVES: The artificial intelligence (AI) field holds significant promise to revolutionize healthcare, including transfusion medicine (TM). This study explored AI use in TM, education and research among International Society of Blood Transfusion (ISBT) members. MATERIALS AND METHODS: A mixed methodology was employed. A survey was conducted June to November 2024. Eighteen participants were interviewed. RESULTS: A total of 218 ISBT members from 67 countries responded to the survey, 43.5% of which use AI. Most users (91.1%) have used Generative AI (GenAI); 82.3% indicated they were self-taught. Application to clinical TM was reported by 54.4%, and 87.3% reported a positive impact. A third of respondents (32.7%) indicated the use of AI in their institutions, commonly GenAI tools. More than two-thirds indicated use in TM education, research or both, and 71.1% indicated a positive impact on their institution's operations. Use in education included preparing lectures and generating questions. Use in research included brainstorming ideas, statistical analysis, coding, data interpretation, manuscript drafting and proofing. Survey respondents reported various challenges in adopting AI, including lack of access to AI resources or expertise (78%), cost (74%), difficulty in hiring AI professionals (73%) and data privacy concerns (72%). Concerns raised during interviews included accuracy of information, regulatory constraints and risks on intellectual ability and employment. CONCLUSION: There is general interest in the use of AI in TM practice, education and research. Barriers to adoption include access to the technology and lack of AI professionals. Educational resources must be expanded. Regulatory constraints and privacy and trust concerns need to be addressed.

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