Latest AI and machine learning research in medical education for healthcare professionals.
The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is transforming diagnostic and therapeutic processes. AI-driven tools, especially in deep learning and machine learning, have shown remarkable potential in enhancing image recognition, analysis and decision-making. This technological advancement allows for...
Education for medical imaging technologists or radiographers in regional and rural areas often faces significant challenges due to limited financial, technological, and teaching resources. Generative AI presents a promising solution to overcome these barriers and support the professional development of radiographers. This pilot study aimed to evaluate the educational value of an in-house AI-based ...
World Health Organization (WHO) estimates 17.9 million deaths globally every year due to Cardiovascular Disease or CVD, which includes an array of dis...
BACKGROUND: Medical simulation is a global trend that improves disease interpretation, diagnostic skills, and clinical abilities, transforming them in...
This work develops a hybrid machine learning/molecular mechanics (ML/MM) interface integrated into the AMBER molecular simulation package. The resulti...
Artificial intelligence (AI) has demonstrated a disruptive ability to enhance and transform clinical medicine. While the dexterous nature of anesthesi...
BACKGROUND: The emergence of ChatGPT has sparked growing interest in its application within university-based dental education. As a conversational lar...
Comprehensive medical assessments are critical for evaluating clinical proficiency in medical education; however, these administrations impose signifi...
The dermatology residency match has become increasingly competitive, culminating in a 2025 United States senior MD match rate of 63 % according to the...
To accelerate optical photon transport simulations in the GATE medical physics framework using a generative adversarial network (GAN), while ensuring ...
An Accreditation and equivalency certificate Verification System is required to ensure integrity, trust, and recognition of qualifications within the ...
Computational Neuroscience (CN) is an interdisciplinary field that combines neuroscience, mathematics, artificial intelligence, theoretical models and...
The gradual research in integrating artificial intelligence in the Dielectrophoresis system is rapid since the evolution of AI in every aspect of tech...
Numerical simulation is the most commonly used method to predict the power generation capacity of EGS during geothermal energy extraction. However, it...
In recent years, integrating artificial intelligence into scientific management in education has transformed traditional methodologies, offering new a...
INTRODUCTION: Understanding the differences and similarities between the different curricula enhances global nursing education practices.
Molecular dynamics (MD) simulation is an important tool and has a wide range of applications in many scientific fields, including drug discovery. Here...
INTRODUCTION: Timely, high-quality feedback is vital in medical education but increasingly difficult due to rising student numbers and limited faculty...
To tackle the challenge of responders heterogeneity, Cognitive Training (CT) research currently leverages AI Techniques for providing individualized c...
Understanding and utilizing AI tools ensure that designers, particularly students, remain involved in a rapidly evolving industry. This study aimed to...