Latest AI and machine learning research in medical education for healthcare professionals.
Evaluation of Large Language Models (LLMs) and their clinical competence has mainly focused on conventional multiple-choice (MCQ) formatted medical question answering exams, yielding benchmarks like MedQA-USMLE, where models have already exceeded expert-level performance. However, alternative assessment methods have recently been proposed, such as SCT-Bench based on Script Concordance Testing (SCT...
The rapid evolution of clinical guidelines and artificial intelligence has created a velocity gap in medical education, where traditional curricula frequently fail to keep pace with professional practice. Consequently, there is an urgent need for formalized artificial intelligence (AI) micro-credentials to ensure workforce readiness across the entire healthcare ecosystem. Furthermore, existing ass...
Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text-to-image (T2I) generation. However, current rewar...
Medical vision-language pretraining (VLP) models have recently been investigated for their generalization to diverse downstream tasks. However, curren...
Multimodal large language models (MLLMs) have achieved remarkable performance across a wide range of vision language tasks. However, their ability in ...
Self-supervised visual pre-training methods face an inherent tension: contrastive learning (CL) captures global semantics but loses fine-grained detai...
This paper introduces a synthetic benchmark to evaluate the performance of vision language models (VLMs) in generating plant simulation configurations...
ABSTRACT Background : The United Arab Emirates (UAE) is characterised by a diverse educational landscape, where students enter medical school from var...
Background and objectives: Colorectal cancer histopathological grading depends on accurate segmentation of glandular structures. Current deep learning...
Pretraining and fine-tuning have emerged as a new paradigm in remote sensing image interpretation. Among them, Masked Autoencoder (MAE)-based pretrain...
Event cameras offer high temporal resolution and low latency, making them ideal sensors for high-speed robotic applications where conventional cameras...
High-fidelity three-dimensional (3D) reconstruction is essential for robotics and simulation. While Neural Radiance Fields (NeRF) and 3D Gaussian Spla...
Genomic language models (gLMs) hold great promise for deciphering biological sequences, yet their effectiveness is hindered by the limited number of e...
Knowledge-Based Visual Question Answering (KB-VQA) requires models to answer questions about an image by integrating external knowledge, posing signif...
In the landscape of modern machine learning, frozen pre-trained models provide stability and efficiency but often underperform on specific tasks due t...
Current video generation models cannot simulate physical consequences of 3D actions like forces and robotic manipulations, as they lack structural und...
We present \textbf{BLOCK}, an open-source bi-stage character-to-skin pipeline that generates pixel-perfect Minecraft skins from arbitrary character co...
Background: Health technology assessment (HTA) agencies issue reimbursement recommendations that determine patient access to new therapies. Predicting...
Compositional scene reconstruction seeks to create object-centric representations rather than holistic scenes from real-world videos, which is nativel...
We present FireRed-OCR, a systematic framework to specialize general VLMs into high-performance OCR models. Large Vision-Language Models (VLMs) have d...