AIMC Topic: Curriculum

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Comparative analysis of AI-generated study guides in otolaryngology education.

American journal of otolaryngology
INTRODUCTION: Resident physicians training in otolaryngology frequently utilize dense traditional textbooks like "Cummings: Otolaryngology Head and Neck Surgery," widely regarded as the gold standard for educational content in the field. However, int...

Understanding and acceptance of open, laparoscopic, and robotic surgery among nursing students: implications for educational curricula based on a mixed-methods study.

Journal of robotic surgery
This study aimed to assess university nursing students' knowledge and perceptions of open, laparoscopic, and robotic surgery applications. A simultaneous sequential nested quantitative-qualitative hybrid research method design was conducted. The sub-...

Preparing Tomorrow's Physicians: The Case for Machine Learning in Medical Education.

Journal of medical systems
Machine learning should be integrated into medical curricula to prepare physicians-in-training for 21st-century practice conditions. This comment proposes practical implementation strategies that build upon existing educational frameworks by drawing ...

Curriculum check, 2025-equipping radiology residents for AI challenges of tomorrow.

Abdominal radiology (New York)
The exponential rise in the artificial intelligence (AI) tools for medical imaging is profoundly impacting the practice of radiology. With over 1000 FDA-cleared AI algorithms now approved for clinical use-many of them designed for radiologic tasks-th...

Revolutionizing radiology education: exploring innovative teaching methods.

Abdominal radiology (New York)
The field of radiology education is undergoing a paradigm shift due to technological advancements and the increasing complexity of medical imaging. Traditional didactic teaching methods are progressively being supplemented or replaced by innovative p...

Macy Foundation Innovation Report Part I: Current Landscape of Artificial Intelligence in Medical Education.

Academic medicine : journal of the Association of American Medical Colleges
The rapid emergence of artificial intelligence (AI), including generative large language models, offers transformative opportunities in medical education. This proliferation has generated numerous speculative discussions about AI's promise but has be...

Assessing medical students' readiness for artificial intelligence after pre-clinical training.

BMC medical education
BACKGROUND: Artificial intelligence (AI) is becoming increasingly relevant in healthcare, necessitating healthcare professionals' proficiency in its use. Medical students and practitioners require fundamental understanding and skills development to m...

Learning to care, caring to learn: the evolving nature of medical education.

The New Zealand medical journal
As Otago Medical School marks its 150th anniversary, this paper reflects on what it means to train doctors for both today and the decades ahead. It traces the school's evolution from its nineteenth-century foundations through key innovations in curri...

Educators' experience and guide to scaffolding generative AI applications throughout a physiology and pharmacology undergraduate laboratory course.

Advances in physiology education
One of the identified points of confusion and a barrier to students using generative artificial intelligence (GenAI) is knowing what their professor would consider appropriate use of GenAI in a classroom setting or course framework. This creates poin...

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 ...