Kelm, Diana

Abstract Background Central venous catheterization (CVC) is a fundamental procedural competency in pulmonary and critical care medicine (PCCM). Although commercially available task trainers are effective for teaching CVC placement, their high cost and logistical requirements can limit access to repeated practice. Objective To evaluate learner and faculty perceptions and usability of a newly developed CVC simulation model compared with a standard commercial task trainer and detail the technical report of the novel model’s construction. Methods We conducted a prospective educational evaluation during the PCCM CVC workshop. Participants practiced CVC placement with both the standard commercial trainer and a locally constructed simulation model developed at our institution. Teaching faculty were asked to evaluate both models from an instructional standpoint. Results Eighteen participants (15 PCCM 1st year fellows and 3 faculty instructors) evaluated the novel CVC simulation model. All fellows (n = 15, 100%) preferred the new model over a standard commercial model, with 14 (93%) reporting improved understanding of CVC insertion steps and 11 (73%) noting ease of ultrasound landmark identification. All fellows (n = 15, 100%) endorsed inclusion of the model in future workshops. Additionally, faculty instructors preferred the novel model for teaching (n = 3, 100%). All faculty supported future use of the model. The initial model development cost was approximately $288.96 with most materials’ reusable. Conclusions The novel task-aligned CVC simulation model offers a realistic, reusable, and cost-effective alternative to commercial simulators, with high learner and faculty preference and strong educational value. Its functional alignment supports integration into fellowship training, with future studies needed to assess objective skill acquisition and retention.

Abstract Large language models are becoming ubiquitous in the editing and generation of written content and are actively being explored for their use in medical education. The use of artificial intelligence (AI) engines to generate content in academic spaces is controversial and has been meet with swift responses and guidance from academic journals and publishers regarding the appropriate use or disclosure of use of AI engines in professional writing. To date, there is no guidance to applicants of graduate medical education programs in using AI engines to generate application content—primarily personal statements and letters of recommendation. In this Perspective, we review perceptions of using AI to generate application content, considerations for the impact of AI in holistic application review, ethical challenges regarding plagiarism, and AI text classifiers. Finally, included are recommendations to the graduate medical education community to provide guidance on use of AI engines in applications to maintain the integrity of the application process in graduate medical education.