Medical Education

Navigating the integration of artificial intelligence in the medical education curriculum: a mixed-methods study exploring the perspectives of medical students and faculty in Pakistan.

BACKGROUND: The integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to eva...

Unveiling GPT-4V's hidden challenges behind high accuracy on USMLE questions: Observational Study.

BACKGROUND: Recent advancements in artificial intelligence, such as GPT-3.5 Turbo (OpenAI) and GPT-4, have demonstrated significant potential by achieving good scores on text-only United States Medical Licensing Examination (USMLE) exams and effectively answering questions from physicians. However, the ability of these models to interpret medical images remains underexplored.

ChatGPT-4 Performance on German Continuing Medical Education-Friend or Foe (Trick or Treat)? Protocol for a Randomized Controlled Trial.

BACKGROUND: The increasing development and spread of artificial and assistive intelligence is opening up new areas of application not only in applied ...

Which curriculum components do medical students find most helpful for evaluating AI outputs?

INTRODUCTION: The risk and opportunity of Large Language Models (LLMs) in medical education both rest in their imitation of human communication. Futur...

Self-supervised 3D medical image segmentation by flow-guided mask propagation learning.

Despite significant progress in 3D medical image segmentation using deep learning, manual annotation remains a labor-intensive bottleneck. Self-superv...

Assessing the disconnect between student interest and education in artificial intelligence in medicine in Saudi Arabia.

BACKGROUND: Although artificial intelligence (AI) has gained increasing attention for its potential future impact on clinical practice, medical educat...

A guide to prompt design: foundations and applications for healthcare simulationists.

Large Language Models (LLMs) like ChatGPT, Gemini, and Claude gain traction in healthcare simulation; this paper offers simulationists a practical gui...

Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.

BACKGROUND: In medical education, enhancing thinking skills is vital. The Virtual Diagnosis and Treatment Platform (VP) refines medical students' diag...

Claude, ChatGPT, Copilot, and Gemini performance versus students in different topics of neuroscience.

Despite extensive studies on large language models and their capability to respond to questions from various licensed exams, there has been limited fo...

Improving quantification accuracy of a nuclear Overhauser enhancement signal at -1.6 ppm at 4.7 T using a machine learning approach.

A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at -1.6 ppm, potentially from choline phospholipids and termed NOE(...

Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.

Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train h...

Frontal fibrosing alopecia part I - Diagnosis and clinical presentation.

Frontal fibrosing alopecia (FFA) is a primary lymphocytic cicatricial alopecia predominantly affecting postmenopausal Caucasian women. It is character...

The integration and innovative practice of intelligent AI and local opera in college teaching.

This paper explores the impacts of integrating AI into the teaching of Chinese Opera using a mixed-methods approach, examining performance, engagement...

Generative artificial intelligence in graduate medical education.

Generative artificial intelligence (GenAI) is rapidly transforming various sectors, including healthcare and education. This paper explores the potent...

A Symbolic AI Approach to Medical Training.

In traditional medical education, learners are mostly trained to diagnose and treat patients through supervised practice. Artificial Intelligence and ...

Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein ...

A Trustworthy Curriculum Learning Guided Multi-Target Domain Adaptation Network for Autism Spectrum Disorder Classification.

Domain adaptation has demonstrated success in classification of multi-center autism spectrum disorder (ASD). However, current domain adaptation method...

HepNet: Deep Neural Network for Classification of Early-Stage Hepatic Steatosis Using Microwave Signals.

Hepatic steatosis, a key factor in chronic liver diseases, is difficult to diagnose early. This study introduces a classifier for hepatic steatosis us...

Predicting the risk of gastroparesis in critically ill patients after CME using an interpretable machine learning algorithm - a 10-year multicenter retrospective study.

BACKGROUND: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinde...

Machine Learning-Enhanced Predictive Modeling for Arbitrary Deterministic Lateral Displacement Design and Test.

The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and...