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Background Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. Objective This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. Methods Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. Results A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students’ learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. Conclusions The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students’ learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.

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This article presents results on the development of a microstructure-based fatigue-crack-initiation model which includes explicit crack-size and microstructure-scale parameters. The current status of microstructure-based fatigue-crack-initiation models is briefly reviewed first. Tanaka and Mura's models [1,2] for crack initiation at slipbands and inclusions are then extended to include crack size and relevant microstructural parameters in the response equations. The microstructure-based model for crack initiation at slipbands is applied to predicting the crack size at initiation, small-crack behavior, and notch fatigue in structural alloys. The calculated results are compared against the experimental data for steels and Al-, Ti-, and Ni-based alloys from the literature to assess the range of predictability and accuracy of the fatigue-crack-initiation model. The applicability of the proposed model for treating variability in fatigue-crack-initiation life due to variations in the microstructure is discussed.

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