Results 181 to 190 of about 1,865,676 (307)

National Learning Objectives in Dermatology and Venereology for the New Swedish Medical Programme. [PDF]

Acta Derm Venereol
Polesie S, Paoli J, Nielsen K, Sonesson A, Shayesteh A, Josefson A, Enerbäck C, Johansson EK, Hoppe T.   +8 more
europepmc   +1 more source

Construction of a Feedback Comment Analysis Model for Evaluation of Endoscopic Surgical Skill

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Surgical education and skill assessments are important in improving surgical skills. However, instructors' comments tend to be complex and unorganized, with varying content and categories. This study aimed to develop a natural language processing (NLP) model to automatically classify feedback comments on surgical procedures and ...
Shusaku Iwai, Shin Takenaka, Daichi Kitaguchi, Hiroki Matsuzaki, Kei Nakajima, Yuya Takabe, Nobuyoshi Takeshita, Masaaki Ito   +7 more
wiley   +1 more source

The application of blended learning in obstetrics teaching: a comprehensive teaching model integrating an intelligent, interactive induction platform. [PDF]

Front Med (Lausanne)
Gu K, Pan H, Dong M, Chen X, Yuan H.
europepmc   +1 more source

Educational Impact of Artificial Intelligence‐Navigation Surgery on Anatomical Landmark Recognition in Medical Students

Annals of Gastroenterological Surgery, EarlyView.
This study evaluated the educational impact of artificial intelligence (AI)‐navigation surgery that provides real‐time anatomical landmark recognition during laparoscopic cholecystectomy for medical students. Thirty students were randomized into surgeon‐guided, self‐learning, and AI‐learning groups, and their performance was assessed using Dice ...
Shigeo Ninomiya, Yusuke Matsunobu, Tabito Oyama, Hiroomi Takayama, Takashi Masuda, Teijiro Hirashita, Yuichi Endo, Tatsushi Tokuyasu, Masafumi Inomata   +8 more
wiley   +1 more source

A BOPPPS-based micro-lecture teaching intervention for orthopaedic postgraduates in China. [PDF]

Med Educ Online
Yang X, Liu T, Li Y, Gao F.
europepmc   +1 more source

principales of learning and teaching methods to clients [PDF]

حسینی آهق, میر محمد
core  

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Machine Learning‐Assisted Infectious Disease Detection in Low‐Income Areas: Toward Rapid Triage of Dengue and Zika Virus Using Open‐Source Hardware

Advanced Intelligent Discovery, EarlyView.
This study introduces an affordable machine learning platform for simultaneous dengue and zika detection using fluorine‐doped tin oxide thin films modified with gold nanoparticles and DNA aptamers. Designed for low‐cost, hardware‐limited devices (< $25), the model achieves 95.3% accuracy and uses only 9.4 kB of RAM, demonstrating viability for resource‐
Marina Ribeiro Batistuti Sawazaki, Bassam Bachour Junior, Marcelo Mulato, José Ilton de Oliveira Filho   +3 more
wiley   +1 more source

Integrating Standardized Patient into Case-Based Learning in Posterior Fossa Meningioma Teaching: Evaluating the Added Value Beyond Case-Based Learning Alone. [PDF]

Adv Med Educ Pract
Zhang S, Zhao C, Shen J.
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source