Background Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly being integrated into health care. As studies on attitudes toward AI have primarily focused on physicians, there is a need to assess the perspectives of students across health care disciplines to inform future curriculum development. Objective This study aims to explore and identify gaps in the knowledge that Canadian health care students have regarding AI, capture how health care students in different fields differ in their knowledge and perspectives on AI, and present student-identified ways that AI literacy may be incorporated into the health care curriculum. Methods The survey was developed from a narrative literature review of topics in attitudinal surveys on AI. The final survey comprised 15 items, including multiple-choice questions, pick-group-rank questions, 11-point Likert scale items, slider scale questions, and narrative questions. We used snowball and convenience sampling methods by distributing an email with a description and a link to the web-based survey to representatives from 18 Canadian schools. Results A total of 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. Overall, 78.77% (1707/2167) predicted that AI technology would affect their careers within the coming decade and 74.5% (1595/2167) reported a positive outlook toward the emerging role of AI in their respective fields. Attitudes toward AI varied by discipline. Students, even those opposed to AI, identified the need to incorporate a basic understanding of AI into their curricula. Conclusions We performed a nationwide survey of health care students across 10 different health professions in Canada. The findings would inform student-identified topics within AI and their preferred delivery formats, which would advance education across different health care professions.

Background Patient‐prosthesis mismatch (PPM) has been identified as a risk factor for mortality and reoperation in patients undergoing surgical aortic valve replacement (SAVR). We present a retrospective analysis of risk factors for PPM and the effects of PPM on early postoperative outcomes after SAVR. Methods Chart review was conducted for patients (N = 3003) undergoing SAVR. PPM was calculated from valve reference orifice areas and patient body surface area. Logistic regression was used to analyze risk factors for PPM and develop a risk score from these results. Regression was also conducted to identify associations between projected PPM status and postoperative outcomes. Results Risk factors for PPM included female sex, higher body mass index (BMI), and use of the St. Jude Epic valve. Patients receiving St. Jude trifecta valves or mechanical valves were less likely to have predicted PPM. We developed a risk score using BMI, sex, and valve type, and retrospectively predicted PPM in our cohort. Mild PPM (odds ratio [OR] = 2.267), severe PPM (OR = 2.869), male sex (OR = 2.091), and younger age (OR = 0.940) were all predictors of SAVR reoperation, while aortic root replacement was associated with reduced reoperation rates (OR = 0.122). Severe PPM carried a risk of in‐hospital mortality (OR = 3.599), and moderate PPM carried a smaller but significant risk (OR = 1.920). Other factors increasing postoperative morbidity and mortality included older age, renal failure, and diabetes. Conclusion PPM could be retrospectively predicted in our cohort using a risk calculation from sex, BMI and valve type. We conclude that all degrees of PPM carry risk for mortality and reoperation.

UNSTRUCTURED Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly integrated into healthcare practice. Many recent commentaries indicated the need to introduce AI literacy training into medical curriculum. However, little is known about what students want to learn about AI, and even less is known from healthcare students outside of medicine. We performed a nation-wide survey of healthcare students across 10 different health professions in Canada. 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. The majority (80%) predicted that AI technology will impact their careers within the coming decade, and 72% reported a positive outlook towards the emerging role of AI in their respective fields. Attitudes towards AI varied by discipline. Findings inform student-identified gaps in knowledge and preferred education delivery formats. This study adds to current literature as it is the first to explore what healthcare students want to learn about AI, and provides insight into future directions for collaboration among sectors such as healthcare, education, and industry.

No abstract