Western Australian medical students’ attitudes towards artificial intelligence in healthcare

• Published in: PloS one Vol. 18; no. 8; p. e0290642
• Main Authors: Stewart, Jonathon, Lu, Juan, Gahungu, Nestor, Goudie, Adrian, Fegan, P. Gerry, Bennamoun, Mohammed, Sprivulis, Peter, Dwivedi, Girish
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 31.08.2023; Public Library of Science (PLoS)
• Subjects: Artificial intelligence; Attitudes; Biology and Life Sciences; Careers; Colleges & universities; Computer and Information Sciences; Computer applications; Core curriculum; Curricula; Deep learning; Distance learning; Education; Forecasts and trends; Health care; Health care industry; Innovations; Knowledge; Literature reviews; Machine learning; Medical education; Medical students; Medicine; Medicine and Health Sciences; Neural networks; Obstetrics; People and Places; Physicians; Polls & surveys; Psychiatry; Qualitative analysis; Radiology; Research and Analysis Methods; Social Sciences; Software; Students; Surveys; Systematic review; Technology application; Training; Australia
• DOI: 10.1371/journal.pone.0290642
• ISSN: 1932-6203

A digital survey instrument was developed based on a review of available literature and consultation with subject matter experts. The survey was piloted with a group of medical students and refined based on their feedback. We then sent this anonymous digital survey to all medical students in WA (approximately 1539 students). Responses were open from the 7.sup.th of September 2021 to the 7.sup.th of November 2021. Students' categorical responses were qualitatively analysed, and free text comments from the survey were qualitatively analysed using open coding techniques. Overall, 134 students answered one or more questions (8.9% response rate). The majority of students (82.0%) were 20-29 years old, studying medicine as a postgraduate degree (77.6%), and had started clinical rotations (62.7%). Students were interested in AI (82.6%), self-reported having a basic understanding of AI (84.8%), but few agreed that they had an understanding of the basic computational principles of AI (33.3%) or the limitations of AI (46.2%). Most students (87.5%) had not received teaching in AI. The majority of students (58.6%) agreed that AI should be part of medical training and most (72.7%) wanted more teaching focusing on AI in medicine. Medical students appeared optimistic regarding the role of AI in medicine, with most (74.4%) agreeing with the statement that AI will improve medicine in general. The majority (56.6%) of medical students were not concerned about the impact of AI on their job security as a doctor. Students selected radiology (72.6%), pathology (58.2%), and medical administration (44.8%) as the specialties most likely to be impacted by AI, and psychiatry (61.2%), palliative care (48.5%), and obstetrics and gynaecology (41.0%) as the specialties least likely to be impacted by AI. Qualitative analysis of free text comments identified the use of AI as a tool, and that doctors will not be replaced as common themes. Medical students in WA appear to be interested in AI. However, they have not received education about AI and do not feel they understand its basic computational principles or limitations. AI appears to be a current deficit in the medical curriculum in WA, and most students surveyed were supportive of its introduction. These results are consistent with previous surveys conducted internationally.