Work Force

Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.

OBJECTIVE: This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.

A Multi-Layered Large Language Model Framework for Disease Prediction

Social telehealth has revolutionized healthcare by enabling patients to share symptoms and receive medical consultations remotely. Users frequently post symptoms on social media and online health platforms, generating a vast repository of medical data that can be leveraged for disease classification and symptom severity assessment. Large language models (LLMs), such as LLAMA3, GPT-3.5 Turbo, and...

Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification

With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Banglades...

BAG: Body-Aligned 3D Wearable Asset Generation

While recent advancements have shown remarkable progress in general 3D shape generation models, the challenge of leveraging these approaches to auto...

Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest

Generative Adversarial Networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated dat...

GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree search

Motivation: Nucleocytoplasmic large DNA viruses (NCLDVs) are notable for their large genomes and extensive gene repertoires, which contribute to the...

Force-Based Robotic Imitation Learning: A Two-Phase Approach for Construction Assembly Tasks

The drive for efficiency and safety in construction has boosted the role of robotics and automation. However, complex tasks like welding and pipe in...

Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models

Mental disorders have become a significant global public health issue, while the shortage of psychiatrists and inefficient training systems severely...

TLXML: Task-Level Explanation of Meta-Learning via Influence Functions

The scheme of adaptation via meta-learning is seen as an ingredient for solving the problem of data shortage or distribution shift in real-world app...

FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting

This paper presents \textbf{FreEformer}, a simple yet effective model that leverages a \textbf{Fre}quency \textbf{E}nhanced Trans\textbf{former} for...

ATRNet-STAR: A Large Dataset and Benchmark Towards Remote Sensing Object Recognition in the Wild

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has signif...

Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition

Understanding the prevalence, disparities, and symptom variations of Post COVID-19 Condition (PCC) for vulnerable populations is crucial to improvin...

InsTALL: Context-aware Instructional Task Assistance with Multi-modal Large Language Models

The improved competence of generative models can help building multi-modal virtual assistants that leverage modalities beyond language. By observing...

Can open source large language models be used for tumor documentation in Germany? -- An evaluation on urological doctors' notes

Tumor documentation in Germany is largely done manually, requiring reading patient records and entering data into structured databases. Large langua...

Med-R$^2$: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicine

Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. Despite their potential, existing works face challenges w...

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs

This paper introduces a promising alternative method for training Generative Adversarial Networks (GANs) on large-scale datasets with clear theoreti...

Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generation

Accurate classification of clinical text often requires fine-tuning pre-trained language models, a process that is costly and time-consuming due to ...

TSVC:Tripartite Learning with Semantic Variation Consistency for Robust Image-Text Retrieval

Cross-modal retrieval maps data under different modality via semantic relevance. Existing approaches implicitly assume that data pairs are well-alig...

Coded Deep Learning: Framework and Algorithm

The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hinderi...

AI for all: bridging data gaps in machine learning and health.

Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health thr...