Latest AI and machine learning research in medical education for healthcare professionals.
Computational Neuroscience (CN) is an interdisciplinary field that combines neuroscience, mathematics, artificial intelligence, theoretical models and experimental data to understand how the brain works. It unravels the intricacies of the nervous system contributing significantly to cognitive science, neuroengineering and machine learning. CN importance in artificial intelligence and medical resea...
The gradual research in integrating artificial intelligence in the Dielectrophoresis system is rapid since the evolution of AI in every aspect of technology since the early 2020s. The benefits of AI integration into DEP systems include improving position and accuracy, faster processing and decision-making, enhancing particle classification, reducing human error, and many others. On the other hand,...
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...
Computer-aided surgical simulation is a critical component of orthognathic surgical planning, where accurately simulating face-bone shape transformati...
Several residency programs have begun investigating artificial intelligence (AI) methods to facilitate application screening processes. However, no u...
Multiple choice questions (MCQs) are frequently used in medical education for assessment. Automated generation of MCQs in board-exam format could pote...
The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the rece...
Deep learning (DL) requires large amounts of labeled data, which is extremely time-consuming and laborintensive to obtain for medical image segmentati...
There is a need to help advance research on using machine learning and data mining techniques in physics education research (PER), which might still b...
BACKGROUND: Postoperative complications in colorectal surgery can significantly impact patient outcomes and healthcare costs. Accurate prediction of t...
Among the broad area of artificial intelligence (AI), generative AI algorithms have emerged as a revolutionary technology able to produce highly reali...
BACKGROUND: Pharmaceutical calculations are required elements of the Doctor of Pharmacy curriculum in the United States. With the growth of artificial...
Developing high-quality pharmacology multiple-choice questions (MCQs) is challenging in large part due to continually evolving therapeutic guidelines ...
The ever-increasing availability of high-throughput DNA sequences and the development of numerous computational methods have led to considerable advan...