Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland

© Author(s) (or their employer(s)) 2022. Reuse permitted under CC BYNC. No commercial reuse. See rights and permissions. Published by BMJ. INTRODUCTION Leading figures in biomedical informatics advocate education in digital health for the healthcare workforce. 2 In healthcare, artificial intelligence/machine learning (AI/ ML)enabled tools increasingly play a role by informing patient triage decisions, clinical decision support systems, and healthcare resource management – advances that are undoubtedly set to grow. Tens of thousands of healthcare apps are available for download by consumers, promising a range of services, from symptom tracking to diagnostic and treatment advice. To date, surveys of medical professionals reveal divergent views about the value and impact of AI/ML on their job with many physicians sceptical about the potential scope for technological innovations on medical tasks. Furthermore, surveys consistently find limited evidence of formal teaching in medical education about AI/ML. Only a few studies – conducted in Europe, the US and South Korea – have explored the formal education and familiarity of medical or healthcare students with respect to digital advances in healthcare, and much of this work consists of single site studies. To better understand and engage with discussion about the benefits, limitations, and ethical dilemmas presented by these tools, today’s medical students will need to become more digitally savvy. Equally, as patients make increasing use of healthcare and wellbeing algorithms, medical students will need to become better prepared to offer patients advice, and to have knowledge about, the robustness of these tools including when algorithms are safe to use. In the present study, we built on this research by assessing the experiences and opinions of final year medical students throughout Ireland about their exposure to AI/ML during their entire degree programme. METHODS A paperbased, crosssectional survey was administered to final year medical students at four of Ireland’s seven medical schools. Institutions were selected in each of the country’s four geographical provinces. The study team devised an original survey instrument to investigate the familiarity, formal exposure to, and opinions of medical students about ML/AI in medicine. We developed the survey instrument in consultation with Irish, British, and American physicians and piloted the survey with physicians in Ireland and the UK (n=6) and final year medical students in the UK (n=5) to ensure face validity. The survey explored students’ experiences and opinions about the teaching of AI/ML in their medical degree programme to date (see Section E of online supplemental appendix 1, and table 1 for survey items). Using ‘yes’ or ‘no’ responses, the survey asked whether students had heard of the term ‘machine learning’, were familiar with “big data analytics”, and whether they had read any academic articles on AI/ML in medicine. Students were requested to estimate both how many hours their instructors or lecturers had spent, and will spend, discussing AI/ML during their degree. In addition, selecting from ‘yes’, ‘no’ or ‘maybe’ responses, the survey inquired whether students planned to learn about how AI/ML as it pertains to medicine. Finally, using a 6point Likert scale, students were requested to rate their level of agreement with the statement ‘Discussion about AI/ML should be part of medical training.’ The institutional review boards at University College Cork [protocol # 2018–188], National University of Ireland Galway [protocol # 19Dec15], Queen’s University Belfast [protocol # 19.28], and University College Dublin [protocol # LS19–89] approved the coright.