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A crossover randomized pilot in preclinical pathology linked a curriculum-aligned custom GPT to higher faculty-assessed self-directed learning documentation and small group performance, though delayed knowledge retention did not differ significantly [2]. A separate study found students rated curated AI answers helpful but struggled to judge their accuracy [9].
Read the rest of this week's brief
On policy, a proposed framework argues that governance in undergraduate medical education centers on conduct restrictions while oversight, infrastructure, and evaluation lag [6]. For graduate training, another proposal maps AI competencies onto the six ACGME core competencies as the council considers revising its Common Program Requirements [14].
Also this week: the section holds ten items on teaching and curriculum, including a mini review of flipped classrooms in dermatology, and two on simulation and skills. One item each covers assessment and feedback, exams and benchmarks, and attitudes and adoption. A comparison of six language models on medical microbiology questions found considerable variation in performance.
The picture as of October 15, 2026; numbered links go to the items below.
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.
A crossover randomized pilot study found a curriculum-aligned custom GPT was linked to higher faculty-assessed self-directed learning documentation and small group performance in pathology, with no significant difference in delayed knowledge retention.
A commentary examines how physician training should be rethought for an era of artificial intelligence, questioning long-standing assumptions in medical education.
A narrative review surveys artificial intelligence in biomedical visualization, including synthetic imaging, vision-language systems, and 3D reconstruction, with attention to uses in medical education and scientific and clinical communication.
Describes using a writing process visualizer to make student drafting visible, offering an alternative to opaque AI-detection scores for preserving take-home essays.
Proposes a multi-level framework for governing artificial intelligence in undergraduate medical education, arguing current policies focus on conduct restrictions while oversight, infrastructure, and evaluation lag.
A quasi-experimental study evaluates a digital radiology simulation module for medical technical assistant trainees in Germany, examining learning gains and how perceived relevance to future work affects them.
A bibliometric analysis of research from 2000 to 2026 finds artificial intelligence in medical education is growing quickly but is concentrated in a few countries and institutions, with themes of learner tools, assessment, and generative.
A study of medical students in a cardiovascular course found curated AI answers helpful, but students struggled to judge accuracy, found responses too detailed, and wanted more diagrams.
A mini review synthesizes evidence on flipped classroom and technology-enhanced learning in dermatology education, distinguishing true flipped classrooms from broader blended learning and discussing implementation and future directions.
A review proposes a stage-specific framework for using large language models across the surgical research workflow, from literature review to manuscript preparation, listing benefits, failure modes, and needed oversight.
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