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Integrating theoretical knowledge with the practical skills essential for clinical practice remains a significant challenge in clinical education. Conventional teaching strategies often fall short in preparing clinicians to navigate the unpredictable, urgent, and multifaceted nature of clinical decision-making, while also providing limited support for the development of cognitive heuristics essential to forming independent clinical judgment. To address these challenges, we introduce vibe coding, a novel AI-assisted, no-code development approach that enables educators to create interactive, customisable learning simulations without programming expertise. By prioritising rapid prototyping and iterative refinement, vibe coding shifts the focus from technical constraints to pedagogical goals, allowing educators to generate code through intuitive, conversational prompts. We applied this approach to develop two distinct applications: the Differential Diagnosis Trainer (DDT), which enhances diagnostic reasoning through randomised clinical scenarios and AI-generated feedback, and the Insulin and Blood Sugar Simulation (IBSS), which offers real-time exploration of metabolic dynamics. Both tools were built using AI-powered no-code platforms, demonstrating significant improvements in accessibility, cost-effectiveness, and scalability. We encourage educators to transition from technology adopters to creators, leveraging AI-driven platforms to develop innovative, scalable, and personalised clinical simulations that transform learning experiences and ultimately enhance patient care.
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http://dx.doi.org/10.1080/0142159X.2025.2488353 | DOI Listing |
J Surg Educ
August 2025
Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkiye.
Objective: While AI-generated feedback has shown promise in medical education, prior studies have only used AI for feedback, with question design handled by human experts, and the process required human involvement. This study aimed to evaluate the effectiveness of a fully automated AI-based system that generates both multiple-choice questions (MCQs) and personalized feedback, without any human input, on improving diagnostic reasoning in preclinical medical students.
Design: A prospective, parallel-group, interventional study.
BioData Min
July 2025
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
Med Teach
April 2025
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Integrating theoretical knowledge with the practical skills essential for clinical practice remains a significant challenge in clinical education. Conventional teaching strategies often fall short in preparing clinicians to navigate the unpredictable, urgent, and multifaceted nature of clinical decision-making, while also providing limited support for the development of cognitive heuristics essential to forming independent clinical judgment. To address these challenges, we introduce vibe coding, a novel AI-assisted, no-code development approach that enables educators to create interactive, customisable learning simulations without programming expertise.
View Article and Find Full Text PDFClin Hemorheol Microcirc
December 2020
Institute of Diagnostic Radiology, University Medical Center Regensburg, Regensburg, Germany.
Aim: To evaluate the use of dynamic contrast enhanced ultrasound (CEUS) with parametric color-coded imaging and time intensity curve analysis (TIC) for planning and follow-up after prostate arterial embolization (PAE).
Material/method: Before and after selective iliacal embolization by PAE with a follow up of 6 months 18 male patients (43-78 years, mean 63±3.5 years) with histopathological proven benign prostate hyperplasia were examined by one experienced examiner.
Adv Genet (Hoboken)
December 2020
Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers.
View Article and Find Full Text PDF