Latest AI and machine learning research in medical education for healthcare professionals.
Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high accuracy on VQA tasks like USMLE MM, their performance on ECG based tasks remains limited, and some models, such as MedGemma, do not support ECG data at all. Interpreting ECGs is inherently challenging, and diagnostic accuracy can vary depending on the ...
Current large vision-language models (LVLMs) typically rely on text-only reasoning based on a single-pass visual encoding, which often leads to loss of fine-grained visual information. Recently the proposal of ''thinking with images'' attempts to alleviate this limitation by manipulating images via external tools or code; however, the resulting visual states are often insufficiently grounded in li...
Background: Large Language Models (LLMs) show promise for clinical decision support in Intensive Care Units (ICU), but their safety and reliability re...
Importance: Large language models (LLMs) have demonstrated diagnostic potential in several medical specialties, but their application to psychiatry - ...
Motivation: Ribosome dynamics are vital in the process of protein expression. Current methods rely on ribosome profiling (Ribo-seq), RNA-seq profiles,...
Accurate brain tumor segmentation from MRI is limited by expensive annotations and data heterogeneity across scanners and sites. We propose a semi-sup...
Semi-supervised learning (SSL) has emerged as a critical paradigm for medical image segmentation, mitigating the immense cost of dense annotations. Ho...
Decision-making in drug development spans heterogeneous stages from molecular design to clinical optimization, yet computer-aided workflows across sta...
The mRNA serves as a crucial bridge between DNA and proteins. Compared to DNA, mRNA sequences are much more concise and information-dense, which makes...
Event-based multimodal large language models (MLLMs) enable robust perception in high-speed and low-light scenarios, addressing key limitations of fra...
World models offer a principled framework for simulating future states under interventions, but realizing such models in complex, high-stakes domains ...
Deploying learned control policies on humanoid robots is challenging: policies that appear robust in simulation can execute confidently in out-of-dist...
Extending 3D Gaussian Splatting (3DGS) to 4D physical simulation remains challenging. Based on the Material Point Method (MPM), existing methods eithe...
Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adapta...
The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult...
Protein design seeks optimal amino acid sequences for target structures, but designing stable protein complexes remains challenging. We introduce a pr...
Content-preserving style transfer, generating stylized outputs based on content and style references, remains a significant challenge for Diffusion Tr...
Purpose: To simulate effective transverse relaxation ($T_2^*$) as a part of MR simulation. $T_2^*$ consists of reversible ($T_2^{\prime}$) and irrever...
Uncertainty in medical image segmentation is inherently non-uniform, with boundary regions exhibiting substantially higher ambiguity than interior are...
Adapting Large Language Models (LLMs) to specialized domains without human-annotated data is a crucial yet formidable challenge. Widely adopted knowle...