Work Force

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 specialists needed by 2030, particularly in low- and middle-income countries (LMICs). Large language models (LLMs) have demonstrated impressive medical reasoning on standardized exams, but their safety, reliability, and specialty-specific performance—especially in procedural fields such as surgery—remain ...

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 healthcare to address physician burnout from documentation burden. However, current implementations primarily rely on real-time audio capture without systematically incorporating longitudinal patient data, potentially limiting documentation completeness for chronic disease management. To compare documen...

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 cardiovascular disease (CVD). Large Language Models (LLMs)...

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, especially during abstract screening. This study evalua...

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—disproportionately limit access for rural populations (60% of US co...

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 colonoscopy remains the gold standard for early detect...

Medical Hallucination in Foundation Models and Their Impact on Healthcare

Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accurac...

Towards Superhuman Imitation Learning for Sequential Head-and-Neck Cancer Treatment Decisions

We propose a simulator-driven imitation learning framework for sequential decision making in head and neck cancer (HNC) treatment. Our method, Superhu...

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-academic settings, which can reduce patients’ access t...

DRB1 Subtyping Reveals Divergent Risk and Protection for Type 1 Diabetes in Middle Eastern Populations

Type 1 diabetes (T1D) is strongly influenced by HLA variation, yet current genetic risk models developed largely in European cohorts perform suboptima...

Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion

The emergence of generative AI and controllable diffusion has made image-to-image synthesis increasingly practical and efficient. However, when inpu...

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 issues related to the shortage and unequal distribut...

Training forecast to football athletes using Hopfield neural networks based on Markov matrix.

This paper proposes a neural network based on the Markov probability transition matrix to predict the training performance of football athletes. First...

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 and retention strategies in rural public health syst...

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 the most common mental disorders. Early mental diso...

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 move beyond time-based metrics, focusing on the comp...

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 progression, evaluating treatment responses, and ...

Addressing Challenges in Data Quality and Model Generalization for Malaria Detection

Malaria remains a significant global health burden, particularly in resource-limited regions where timely and accurate diagnosis is critical to effe...

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 mental disorders. Early mental disorder detection can ...