Latest AI and machine learning research in medical education for healthcare professionals.
The GluN1/GluN2A N-methyl-D-aspartate receptor (NMDAR) is a critical ligand-gated ion channel in the central nervous system, playing essential roles in synaptic plasticity, learning, and memory. Understanding its dynamics in the open/active state is paramount for deciphering its physiological functions and for developing targeted therapeutics. Despite many past efforts, the active/open state has n...
Detecting mass extinction events from phylogenies is a fundamental yet challenging task. While traditional likelihood-based methods are available, deep learning offers a powerful, simulation-based alternative. Here, we evaluate a deep learning approach using a novel hybrid model that combines Graph Neural Networks with Long Short-Term Memory networks. This model analyzes phylogenies—containing bot...
Post-translational modifications (PTMs) are covalent changes in proteins after biosynthesis that shape stability, localization, and function. While nu...
Deep generative models for protein structure and sequence are increasingly used to design proteins with therapeutic and industrial applications, but t...
Biologically-informed neural networks (BiNNs) offer interpretable deep learning models for biological data, but the dataset characteristics required f...
Musculoskeletal dynamics influence the progression and rehabilitation of many movement-related conditions. However, accurately estimating whole-body d...
The variability in responses generated by Large Language Models (LLMs) like OpenAI’s GPT-4 poses challenges in ensuring consistent accuracy on medical...
DeepSeek, a Chinese artificial intelligence company, released its first free chatbot app based on its DeepSeek-R1 model. DeepSeek provides its models,...
Artificial intelligence (AI) has transformed medical education through optimized instruction, competency assessment, and personalized learning. Its in...
Artificial intelligence (AI) is increasingly playing a crucial role in modern medicine, particularly in clinical decision support. This study compares...
Assessing medical student performance in Objective Structured Clinical Examinations (OSCEs) is labor-intensive, requiring trained evaluators to review...
Rapid innovation and new regulations increase the need for post-marketing surveillance of implantable devices. However, complex multi-level confoundin...
During residency, each resident is observed and receives feedback based on their performance. Residency training is demanding, with some residents str...
The integration of evidence-based reasoning with retrieval-augmented generation (GraphRAG) holds great promise for enhancing large language model (LLM...
Coronary revascularization decision-making can be challenging. While artificial intelligence (AI) models have been developed to support this decision-...
Recent advances in large language models (LLMs) show promise in clinical applications, but their performance in women’s health remains underexamined 1...
Educating clinicians about Artificial Intelligence (AI) is an urgent need(1) as the UK General Medical Council (GMC) places liability with practitione...
To determine the impact of the temperature parameter on GPT-4o’s diagnostic accuracy when evaluating emergency medicine cases and assess the effect on...
Diagnostic errors remain a pervasive yet preventable source of patient harm, with resourcelimited healthcare systems in low- and middle-income countri...
Healthcare professionals face an urgent need for AI literacy as artificial intelligence technologies rapidly transform clinical practice, yet nursing-...