State of the Field
Recent advancements in medical AI are increasingly focused on enhancing diagnostic accuracy and operational efficiency across various domains. For instance, the development of Medical SAM3 has improved medical image segmentation by fine-tuning a foundation model on diverse datasets, enabling robust performance even in complex anatomical scenarios. Meanwhile, the consolidation of Type 1 Diabetes datasets into the MetaboNet resource addresses fragmentation, facilitating more reliable algorithm development and potentially improving patient management. In neuroimaging, innovations in ultra-low-field diffusion tensor imaging leverage deep learning for artifact correction and super-resolution, promising broader access to neuroimaging capabilities. Additionally, the introduction of unified models like MedVL-SAM2 and Self-MedRAG highlights a trend toward integrating multimodal reasoning and iterative learning in clinical applications, enhancing the reliability of AI systems in high-stakes environments. Collectively, these efforts reflect a shift toward more adaptable, data-driven solutions that aim to bridge the gap between AI capabilities and clinical needs.
Papers
1–10 of 50Enhanced Portable Ultra Low-Field Diffusion Tensor Imaging with Bayesian Artifact Correction and Deep Learning-Based Super-Resolution
Portable, ultra-low-field (ULF) magnetic resonance imaging has the potential to expand access to neuroimaging but currently suffers from coarse spatial and angular resolutions and low signal-to-noise ...
MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management
Progress in Type 1 Diabetes (T1D) algorithm development is limited by the fragmentation and lack of standardization across existing T1D management datasets. Current datasets differ substantially in st...
Medical SAM3: A Foundation Model for Universal Prompt-Driven Medical Image Segmentation
Promptable segmentation foundation models such as SAM3 have demonstrated strong generalization capabilities through interactive and concept-based prompting. However, their direct applicability to medi...
Explainable Deep Learning for Pediatric Pneumonia Detection in Chest X-Ray Images
Background: Pneumonia remains a leading cause of morbidity and mortality among children worldwide, emphasizing the need for accurate and efficient diagnostic support tools. Deep learning has shown str...
MedVL-SAM2: A unified 3D medical vision-language model for multimodal reasoning and prompt-driven segmentation
Recent progress in medical vision-language models (VLMs) has achieved strong performance on image-level text-centric tasks such as report generation and visual question answering (VQA). However, achie...
A Multi-scale Linear-time Encoder for Whole-Slide Image Analysis
We introduce Multi-scale Adaptive Recurrent Biomedical Linear-time Encoder (MARBLE), the first \textit{purely Mamba-based} multi-state multiple instance learning (MIL) framework for whole-slide image ...
Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement
Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communities, and accurate reco...
MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery
General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and performance required for drug discovery tasks. Simply increasing ...
CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation
Medical vision-language models can automate the generation of radiology reports but struggle with accurate visual grounding and factual consistency. Existing models often misalign textual findings wit...
Organ-Aware Attention Improves CT Triage and Classification
There is an urgent need for triage and classification of high-volume medical imaging modalities such as computed tomography (CT), which can improve patient care and mitigate radiologist burnout. Study...