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Language associated with large language models rose 17.7% in 2024 US residency personal statements, about one in six additional statements [5]. Use is also reaching clinical settings: residents still favor UpToDate but frequently use OpenEvidence, and the effect on care and learning is unstudied [6].
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A Riyadh survey of physicians and dentists found high awareness of clinical AI tools but only moderate adoption intent, limited by low trust [2]. On the teaching side, a review argues clinical reasoning should become a formal, longitudinal core subject [3], and a commentary warns that sycophantic AI can erode tolerance for critical feedback [10].
Also this week: six items on attitudes and adoption, three each on simulation and skills and on assessment and feedback, two on teaching and curriculum, and one on exams and benchmarks. One study found ChatGPT-designed simulation scenarios took less time to create than faculty-facilitated designs, with comparable quality.
The picture as of October 19, 2026; numbered links go to the items below.
An npj Digital Medicine paper examines using large language models to generate synthetic OSCE objective structured clinical examination cases for medical education.
A survey of physicians and dentists in Riyadh found high awareness of clinical AI tools but only moderate adoption intent, limited by low trust, and calls for training and regulatory support.
A review argues clinical reasoning should become a formal, longitudinal core subject across medical education because AI tools make these skills more necessary, not less.
A comparative study found large language models performed well on expert-reviewed pediatric infectious disease scenarios, but recommends use only within clinician-supervised workflows.
Language associated with large language models rose 17.7% in 2024 US residency personal statements, about one in six additional statements.
An institutional survey finds residents still favor UpToDate but frequently use the AI tool OpenEvidence in clinical settings, with impact on care and learning still unstudied.
A study compares AI-generated and expert-authored dermatology exam questions as perceived by medical students and physicians.
A cross-sectional survey of urology trainees evaluates how large language models are adopted, how effective trainees find them, and what limitations they report.
A structural equation modeling study in Saudi Arabia examines how dependence on AI tools relates to academic self-efficacy and depressive symptoms among medical students.
An npj Digital Medicine commentary argues sycophantic AI can erode tolerance for critical feedback and makes the case for productive friction in medical training.
Educators from the International Competency-based Education blog discuss how a strong educational alliance between trainee and supervisor builds trust in competency-based health professions education, including feedback seeking.
A mixed methods study built a consultant-reviewed English-Malay clinical dialogue dataset and fine-tuned a large language model for simulated history-taking, finding the approach feasible without establishing educational superiority.
Universities are hiring more philosophy professors to study questions about knowledge and humanity raised by artificial intelligence, creating rising demand for the field.
Newcastle and Westminster universities are not renewing their Turnitin plagiarism-detection contracts, following Southampton, amid questions about how the service handles AI-generated writing.
A study compared simulation scenarios designed by medical students using ChatGPT with faculty-facilitated designs, finding the AI-generated scenarios took less time to create and were comparable in quality.