AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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Patient Autonomy in Medical Education: Navigating Ethical Challenges in the Age of Artificial Intelligence.

Inquiry : a journal of medical care organization, provision and financing
The increasing integration of Artificial Intelligence (AI) in the medical domain signifies a transformative era in healthcare, with promises of improved diagnostics, treatment, and patient outcomes. However, this rapid technological progress brings a...

Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications.

JMIR medical education
BACKGROUND: Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical educati...

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties.

BMC medical education
BACKGROUND: The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary st...

Surface and deep learning: a blended learning approach in preclinical years of medical school.

BMC medical education
BACKGROUND: Significant challenges are arising around how to best enable peer communities, broaden educational reach, and innovate in pedagogy. While digital education can address these challenges, digital elements alone do not guarantee effective le...

Artificial intelligence in medical imaging education: Recommendations for undergraduate curriculum development.

Radiography (London, England : 1995)
OBJECTIVES: Artificial intelligence (AI) is rapidly being integrated into medical imaging practice, prompting calls to enhance AI education in undergraduate radiography programs. Combining evidence from literature, practitioner insights, and industry...

Medical imaging and radiation science students' use of artificial intelligence for learning and assessment.

Radiography (London, England : 1995)
INTRODUCTION: Artificial intelligence has permeated all aspects of our existence, and medical imaging has shown the burgeoning use of artificial intelligence in clinical environments. However, there are limited empirical studies on radiography studen...

GSSCL: A framework for Graph Self-Supervised Curriculum Learning based on clustering label smoothing.

Neural networks : the official journal of the International Neural Network Society
Graph self-supervised learning is an effective technique for learning common knowledge from unlabeled graph data through pretext tasks. To capture the interrelationships between nodes and their essential roles globally, existing methods use clusterin...

Exploring the integration of artificial intelligence in radiology education: A scoping review.

Current problems in diagnostic radiology
BACKGROUND: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current lands...