Latest AI and machine learning research in medical education for healthcare professionals.
As the use of artificial intelligence (AI) continues to grow in radiology, it has become clear that its real-world performance often differs from that demonstrated in premarket testing, underscoring the need for robust quality management (QM) programs at local institutions. For decades, a key mechanism to ensure QM in radiology practices has been ACR accreditation. However, no such program current...
Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, traditional approaches, such as sensitivity and uncertainty analyses, are computationally intensive and inefficient, as they require numerous model runs....
BACKGROUND: The use of artificial intelligence (AI) technologies in radiography practice is increasing. As this advanced technology becomes more embed...
Advances in artificial intelligence (AI) technologies have not been widely integrated into simulation education. This work examines the process of des...
Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreatio...
BACKGROUND: The purpose of this study was to evaluate the performance of widely used artificial intelligence (AI) chatbots in answering prosthodontics...
After completing medical school in the United States, most students apply to residency programs to progress in their training. The residency applicati...
Since the publication of "What is the Current and Future Status of Digital Mental Health Interventions?" the exponential growth and widespread adoptio...
Keratinocyte carcinoma, including basal cell and squamous cell carcinoma, is the most prevalent form of cancer within the United States, with its inci...
Holistic review has been widely adopted in medical education as a means of promoting equity in the application process and diversity in the medical wo...
PURPOSE: This study explores a self-learning method as an auxiliary approach in residency training for distinguishing between benign and malignant thy...
BACKGROUND: The integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practic...
BACKGROUND: Recent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achie...
BACKGROUND: The increasing development and spread of artificial and assistive intelligence is opening up new areas of application not only in applied ...
INTRODUCTION: The risk and opportunity of Large Language Models (LLMs) in medical education both rest in their imitation of human communication. Futur...
Despite significant progress in 3D medical image segmentation using deep learning, manual annotation remains a labor-intensive bottleneck. Self-superv...
BACKGROUND: Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical educat...
Large Language Models (LLMs) like ChatGPT, Gemini, and Claude gain traction in healthcare simulation; this paper offers simulationists a practical gui...
BACKGROUND: In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students' diag...
Despite extensive studies on large language models and their capability to respond to questions from various licensed exams, there has been limited fo...