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Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education.
BMC Med Educ
January 2025
Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, 410000, China.
Introduction: Artificial intelligence technology has a wide range of application prospects in the field of medical education. The aim of the study was to measure the effectiveness of ChatGPT-assisted problem-based learning (PBL) teaching for urology medical interns in comparison with traditional teaching.
Methods: A cohort of urology interns was randomly assigned to two groups; one underwent ChatGPT-assisted PBL teaching, while the other received traditional teaching over a period of two weeks.
Objective: To determine if surgical skills instructors' experience and qualifications influence students' learning of small animal ovariohysterectomy on a model (mOVH).
Sample Population: Second-year veterinary students (n = 105).
Methods: Students were randomized to three groups, taught by: (1) residency-trained surgeons with over 3 years' experience teaching mOVH, (2) general practitioners with over 3 years' experience teaching mOVH (GP >3), and (3) general practitioners with under 3 years' experience (GP <3).
Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition.
Diagnostics (Basel)
December 2024
Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Autism spectrum disorder (ASD) is a group of developmental disorders characterized by poor social skills, low motivation in activities, and a lack of interaction with others. Traditional intervention approaches typically require support under the direct supervision of well-trained professionals. However, teaching and training programs for children with ASD can also be enhanced by assistive technologies, artificial intelligence, and robotics.
View Article and Find Full Text PDFPredictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?
BMC Endocr Disord
January 2025
Department of Public Health Studies, Elon University, Elon, NC, USA.
Background: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, this study specifically investigated mortality predictors among these patients.
View Article and Find Full Text PDFDeep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.
Sci Rep
January 2025
Department of Orthopaedic Surgery and Traumatology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate computed tomography (CT)-based three-dimensional techniques, are limited by cost and radiation exposure. In this study we propose deep learning-based methods that enable automatic scapular morphological analysis from diagnostic MRI despite the anisotropic resolution and reduced field of view, compared to CT.
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