US Health Policy

Plasmonic SPR metamaterial sensor optimized via machine learning for sensitive organic chemical detection in wastewater.

The precise identification and assessment of organic chemical compounds from polluted water have become crucial for managing their environmental impact. Biosensors have numerous benefits, which include barely preparation of samples, quick assessment durations, substantial specificity and sensitivity, as well as low limits of detection. This Proposed research demonstrates a Solid Square-Ring Perfec...

GAST-NET: A multi-modal and multi-task deep learning framework for preoperative prediction of perineural invasion and prognostic risk in gastric cancer.

BACKGROUND: Preoperative imaging prediction of perineural invasion in gastric cancer (GC-PNI) mainly relies on tumour characteristics and clinical variables, while the potential of non-tumour-derived multimodal features remains underexplored. METHOD: We retrospectively enrolled 777 patients from three medical centers and divided them into a training cohort, an internal testing cohort (I-T), and tw...

Development of a Predictive Calculator for the Need for Abdominoperineal Resection after Chemoradiation Therapy in Anal Squamous Cell Carcinoma.

BACKGROUND: Chemoradiation therapy (CRT) is standard treatment for anal squamous cell carcinoma (ASCC). Abdominoperineal resection (APR) is usually re...

Minor salivary gland biopsy in Sjogren's disease: a diagnostic and prognostic tool toward precision medicine.

Minor salivary gland biopsy occupies a distinctive position in the evaluation of Sjögren disease (SjD), offering diagnostic and prognostic insights th...

Integrative transcriptomic and machine learning framework reveals candidate genes and potential mechanisms of aflatoxin B1 exposure in breast cancer.

Aflatoxin B1 (AFB1), a known mycotoxin and environmental hazard, has been linked to breast cancer, yet the exact biological pathways remain poorly cha...

Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.

BACKGROUND: Artificial intelligence (AI) has shown increasing potential in lung cancer imaging, particularly in detection, staging, prognosis, and rec...

Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies o...

Biosimilar stewardship at the interface of disproportionality methods and AI integration: current evidence and potential strategies.

INTRODUCTION: Biosimilar stewardship includes safety, appropriate use, and value. However, it faces challenges due to shortages in disproportionality ...

Mechanistic interpretability of reinforcement learning in Medicaid care coordination.

OBJECTIVE: To expose reasoning pathways of a reinforcement learning policy for Medicaid care coordination, develop an error taxonomy and implement fai...

Deep Learning Identification of Clear Cell Renal Cell Carcinoma on MR Imaging.

OBJECTIVE: To investigate the performance of various convolutional neural networks (CNNs) in identifying clear renal cell carcinoma (ccRCC) on MRI and...

Multiple neural networks from cognition to motivation of prosocial behaviour in rodents: Potential mechanism between empathy and autism spectrum disorder.

Prosocial behaviour, as a facet of social behaviour across species, entails voluntary actions that benefit others, including helping and comforting be...

Financial Reasoning in Radiology: Interpreting Risk, Value, and Capital Allocation for Resilient and High-Value Imaging Services.

Radiology is among the most capital-intensive specialities in healthcare, relying on high-cost imaging equipment, complex information technology infra...

Integrating causal inference and machine learning to quantify climate-malaria relationships: Evidence of temperature and rainfall thresholds from Colombian municipalities.

Rainfall and temperature are key climate determinants of malaria incidence; yet their causal exposure-response curves on malaria incidence across the ...

Interpretable machine learning-driven identification of novel DENV NS2B-NS3 protease inhibitors through multi-stage virtual screening and experimental validation.

Dengue fever is a mosquito-borne viral infection caused by dengue virus (DENV). It has emerged as a worldwide health problem, afflicting millions of p...

Medicare advantage becoming a disadvantage with use of artificial intelligence in prior authorization review.

Reported instances of AI-assisted, blanket denials of coverage have increased in recent years, particularly for Medicare Advantage plans, resulting in...

Electronic health record use in physiotherapy: adoption, perceived relevance and utilisation patterns-a cross-sectional study.

OBJECTIVES: This study aims to assess electronic health record (EHR) use in physiotherapy, identify factors influencing its adoption and evaluate phys...

Automated detection of stigmatizing language in Electronic Health Records (EHRs) using a multi-stage transfer learning approach.

OBJECTIVE: Stigmatizing language (SL) in Electronic Health Records (EHRs) can perpetuate biases and negatively impact patient care. This study introdu...

An Explainable Machine Learning Model for Predicting Short-Term Haemodynamic Changes Post-TIPS With Prognostic Implications.

BACKGROUND AND AIMS: While remeasuring portacaval pressure gradient (PPG) after transjugular intrahepatic portosystemic shunt (TIPS), it provides supe...

At the Crossroads of Data Justice and Data Capitalism: How Generative AI in Healthcare Mobilises Its Assemblages.

Algorithmic technologies such as machine learning, generative artificial intelligence (GenAI) and automated decision-making have become one of the fro...

Performance of an adult-trained AI tool for intracranial hemorrhage detection on head CT in children aged 6-17 years.

BACKGROUND: Most commercially available artificial intelligence (AI) tools in radiology are trained and approved for adult use, creating an access gap...