AIMC Topic: Students, Medical

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Is AI the future of evaluation in medical education?? AI vs. human evaluation in objective structured clinical examination.

BMC medical education
BACKGROUND: Objective Structured Clinical Examinations (OSCEs) are widely used in medical education to assess students' clinical and professional skills. Recent advancements in artificial intelligence (AI) offer opportunities to complement human eval...

Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey.

BMC medical education
INTRODUCTION: Artificial intelligence is a transformative tool for improving healthcare delivery and diagnostic accuracy in the medical and dental fields. This study aims to assess the readiness of future healthcare workers for artificial intelligenc...

A Comparative Bicentric Study on Ultrasound Education for Students: App- and AI-Supported Learning Versus Traditional Hands-on Instruction (AI-Teach Study).

Academic radiology
BACKGROUND: The integration of artificial intelligence (AI) into medical education presents significant opportunities for enhancing teaching methods and student learning outcomes. Despite its potential benefits, the implementation of AI in curricula ...

Integrating AI in medical education: a comprehensive study of medical students' attitudes, concerns, and behavioral intentions.

BMC medical education
BACKGROUND: To analyze medical students' perceptions, trust, and attitudes toward artificial intelligence (AI) in medical education, and explore their willingness to integrate AI in learning and teaching practices.

Exploring the influence of artificial intelligence integration on personalized learning: a cross-sectional study of undergraduate medical students in the United Kingdom.

BMC medical education
BACKGROUND: With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI...

Development of a GPT-4-Powered Virtual Simulated Patient and Communication Training Platform for Medical Students to Practice Discussing Abnormal Mammogram Results With Patients: Multiphase Study.

JMIR formative research
BACKGROUND: Standardized patients (SPs) prepare medical students for difficult conversations with patients. Despite their value, SP-based simulation training is constrained by available resources and competing clinical demands. Researchers are turnin...

AI as teacher: effectiveness of an AI-based training module to improve trainee pediatric fracture detection.

Skeletal radiology
OBJECTIVE: Prior work has demonstrated that AI access can help residents more accurately detect pediatric fractures. We wished to evaluate the effectiveness of an unsupervised AI-based training module as a pediatric fracture detection educational too...

Large Language Models in Biochemistry Education: Comparative Evaluation of Performance.

JMIR medical education
BACKGROUND: Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have started a new era of innovation across various fields, with medicine at the forefront of this technological revolution. Many studies i...

Virtual escape rooms in anatomy education: case studies from two institutions.

Advances in physiology education
Virtual escape rooms (ERs) require learners to solve puzzles and answer riddles while trying to "escape" a digital room. Although the educational merit of such gamified learning activities continues to be realized, guides on the development of ERs ar...

Evaluating the value of AI-generated questions for USMLE step 1 preparation: A study using ChatGPT-3.5.

Medical teacher
PURPOSE: Students are increasingly relying on artificial intelligence (AI) for medical education and exam preparation. However, the factual accuracy and content distribution of AI-generated exam questions for self-assessment have not been systematica...