Latest AI and machine learning research in medical education for healthcare professionals.
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) has rapidly transformed radiology practice, yet structured and practical AI education remains inconsistently integrated into radiology residency training. We developed and implemented a hands-on AI curriculum designed to integrate core computational principles with clinically relevant imaging applications. This study describes the curriculum d...
BACKGROUND: Code status discussions (CSDs) are essential in clinical practice yet training modalities are often resource-intensive and not widely available. AIM: To evaluate whether artificial intelligence (AI)-driven simulation can enhance CSD training. SETTING: A public safety-net hospital affiliated with an academic medical center. PARTICIPANTS: Postgraduate year (PGY)-2 and PGY-3 internal medi...
BACKGROUND: Ophthalmology training requires visual interpretation, procedural skill, and supervised clinical reasoning, but trainee volume, faculty av...
BACKGROUND: Large language model (LLM)-based AI teaching agents are increasingly used in medical education, yet their pedagogical quality is typically...
Artificial intelligence (AI) is rapidly transforming histopathology, with applications ranging from workflow optimisation and quality assurance to tum...
RATIONALE AND OBJECTIVES: Radiology residency often fails to account for individual differences between residents or provide sufficient exposure to di...
STUDY OBJECTIVE: To apply Autor's labor economics task framework to classify emergency physician tasks by automation susceptibility and map current ar...
Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissection; however, decisions to intervene on asc...
BACKGROUND: The rapid advancement of digital technologies has transformed healthcare delivery, creating an imperative for medical education to integra...
Individuals with Color Vision Deficiency (CVD) face difficulties in accurately distinguishing between colors due to reduced perceptual contrast. To ad...
OBJECTIVES: This systematic review aimed to investigate the current development and application landscape of generative artificial intelligence (Gen-A...
Purpose To evaluate the effect of an artificial intelligence (AI)-driven CT queue prioritization system on emergency department CT wait times using a ...
INTRODUCTION: The application of Large Language Models (LLMs) for automated question generation in dental education is hindered by inefficient data ma...
PURPOSE: We proposed a method that combines the deep learning model U-Net with a dendritic neuron model (DNM) and demonstrated its effectiveness for m...
This study aimed to explore the association between medical students' critical thinking (CT) disposition and learning approach (LA), to provide novel ...
This quasi-experimental study examined whether teacher-guided, ChatGPT-supported social studies instruction was associated with middle school students...
Supervised synthetic computed tomography (sCT) generation from cone-beam CT (CBCT) requires spatially registered training pairs, yet perfect registrat...
As dementia prevalence rises globally, health sciences education must evolve to prepare future professionals across disciplines to provide person-cent...
This 2026 update of the European Society of Cardiology (ESC) Core Curriculum for the Cardiologist reflects contemporary and emerging requirements for ...
INTRODUCTION: Large language models (LLMs) are increasingly proposed as deductive coders in qualitative research, but their measurement properties rem...