Medicare

Clinical Applications of Artificial Intelligence in Neurology Practice.

As artificial intelligence (AI) tools become increasingly mainstream, they can potentially transform neurology clinical practice by improving patient care and reducing clinician workload. However, with these promises also come perils, and neurologists must understand AI as it becomes integrated into health care. This article presents a brief background on AI and explores some of the potential appl...

Conformal uncertainty quantification to evaluate predictive fairness of foundation AI model for skin lesion classes across patient demographics

Deep learning based diagnostic AI systems based on medical images are starting to provide similar performance as human experts. However these data hungry complex systems are inherently black boxes and therefore slow to be adopted for high risk applications like healthcare. This problem of lack of transparency is exacerbated in the case of recent large foundation models, which are trained in a se...

Learning to Reason for Long-Form Story Generation

Generating high-quality stories spanning thousands of tokens requires competency across a variety of skills, from tracking plot and character arcs t...

Post-Incorporating Code Structural Knowledge into LLMs via In-Context Learning for Code Translation

Code translation migrates codebases across programming languages. Recently, large language models (LLMs) have achieved significant advancements in s...

M-DocSum: Do LVLMs Genuinely Comprehend Interleaved Image-Text in Document Summarization?

We investigate a critical yet under-explored question in Large Vision-Language Models (LVLMs): Do LVLMs genuinely comprehend interleaved image-text ...

LOCORE: Image Re-ranking with Long-Context Sequence Modeling

We introduce LOCORE, Long-Context Re-ranker, a model that takes as input local descriptors corresponding to an image query and a list of gallery ima...

Utsarjan: A smartphone App for providing kidney care and real-time assistance to children with nephrotic syndrome

Background Telemedicine has the potential to provide secure and cost-effective healthcare at the touch of a button. Nephrotic syndrome is a chronic ...

Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction

Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey method...

RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task

High-resolution remote sensing analysis faces challenges in global context modeling due to scene complexity and scale diversity. While CNNs excel at...

Beyond Words: Advancing Long-Text Image Generation via Multimodal Autoregressive Models

Recent advancements in autoregressive and diffusion models have led to strong performance in image generation with short scene text words. However, ...

LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text

This study addresses the technical bottlenecks in handling long text and the "hallucination" issue caused by insufficient short text information in ...

ELM: Ensemble of Language Models for Predicting Tumor Group from Pathology Reports

Population-based cancer registries (PBCRs) face a significant bottleneck in manually extracting data from unstructured pathology reports, a process ...

Dynamically Learning to Integrate in Recurrent Neural Networks

Learning to remember over long timescales is fundamentally challenging for recurrent neural networks (RNNs). While much prior work has explored why ...

Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding

Despite advanced token compression techniques, existing multimodal large language models (MLLMs) still struggle with hour-long video understanding. ...

Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation

Scene Graph Generation (SGG) aims to represent visual scenes by identifying objects and their pairwise relationships, providing a structured underst...

Does Your Vision-Language Model Get Lost in the Long Video Sampling Dilemma?

The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains...

CHOrD: Generation of Collision-Free, House-Scale, and Organized Digital Twins for 3D Indoor Scenes with Controllable Floor Plans and Optimal Layouts

We introduce CHOrD, a novel framework for scalable synthesis of 3D indoor scenes, designed to create house-scale, collision-free, and hierarchically...

Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework

Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection...

MTV-Inpaint: Multi-Task Long Video Inpainting

Video inpainting involves modifying local regions within a video, ensuring spatial and temporal consistency. Most existing methods focus primarily o...

Context-Aware Rule Mining Using a Dynamic Transformer-Based Framework

This study proposes a dynamic rule data mining algorithm based on an improved Transformer architecture, aiming to improve the accuracy and efficienc...