Work Force

Pretraining Diversity and Clinical Metric Optimization Achieve State-of-the-Art Performance on ChestX-ray14

We achieved state-of-the-art performance on the NIH ChestX-ray14 multi-label classification task usi...

Physician Readiness for AI in Primary Care: A Cross-Sectional Survey on the Knowledge-Attitude Gap and Implementation Priorities in Switzerland

Primary care artificial intelligence adoption among United States (US) physicians accelerated from 3...

Probing the Surgical Competence of LLMs: A global health study leveraging AfriMedQA benchmarks

Global surgical care faces a severe workforce shortage, with more than 1.2 million additional specia...

Ambient Only vs. Longitudinal Data-Enhanced AI Documentation: A Pilot Study Quantifying the Value of Historical Clinical Context in Primary Care

Ambient artificial intelligence (AI) clinical documentation tools have gained rapid adoption in heal...

The Promise and Peril of Large Language Models in Digital Health: GPT-4 Personalizes Cardiovascular Patient Education but Amplifies Gender Biases

Gender-neutral patient education materials often overlook critical sex-based differences in cardiova...

LLM-based Multi-Agent Collaboration for Abstract Screening towards Automated Systematic Reviews

Systematic reviews (SRs) are essential for evidence-based practice but remain labor-intensive, espec...

Accurate, Race-Free LDL-C Estimation in Non-Fasting Settings: A Machine-Learning Study in 3,477 Adults

Traditional LDL-C testing barriers—mandatory 9–12 hour fasting and inperson visits—disproportionatel...

Evaluating Large Language Models for Colonoscopy Preparation Assistance: Correctness and Diversity in Synthetic Dialogues

Colorectal cancer is the third leading cause of cancer-related deaths in the United States, and colo...

LLM-Assisted Taxonomy and Temporal Analysis of Provider Questions About HIV in provider-to-provider telehealth

Ongoing education in HIV care is limited for many healthcare providers working in rural and non-acad...

Optimizing patient check-in process for telehealth visits: a data-driven perspective.

Telemedicine in China: Effective indicators of telemedicine platforms for promoting health and well-being among healthcare consumers.

OBJECTIVE: Telemedicine platforms played a crucial role during the COVID-19 pandemic, alleviating is...

Leveraging big data in health care and public health for AI driven talent development in rural areas.

INTRODUCTION: This study proposes a novel Transformer-based approach to enhance talent attraction an...

Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.

CONTEXT AND BACKGROUND: Depression has affected millions of people worldwide and has become one of t...

Secure messaging telehealth billing in the digital age: moving beyond time-based metrics.

OBJECTIVE: We proposed adopting billing models for secure messaging (SM) telehealth services that mo...

Automated Deep Learning-Based Detection and Segmentation of Lung Tumors at CT.

Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer...

GPT-4 on Clinic Depression Assessment: An LLM-Based Pilot Study

Depression has impacted millions of people worldwide and has become one of the most prevalent ment...

Single-image reflection removal via self-supervised diffusion models

Reflections often degrade the visual quality of images captured through transparent surfaces, and ...

Motion Transfer-Driven intra-class data augmentation for Finger Vein Recognition

Finger vein recognition (FVR) has emerged as a secure biometric technique because of the confident...

VideoMaker: Zero-shot Customized Video Generation with the Inherent Force of Video Diffusion Models

Zero-shot customized video generation has gained significant attention due to its substantial appl...

Dissecting CLIP: Decomposition with a Schur Complement-based Approach

The use of CLIP embeddings to assess the alignment of samples produced by text-to-image generative...

Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts.

"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and...