Latest AI and machine learning research in medicare for healthcare professionals.
Clinical trial enrollment in oncology remains limited by increasingly complex eligibility criteria, biomarker stratification, and fragmented clinical data, contributing to prolonged recruitment timelines and low participation rates. This review examines contemporary pre-screening and screening approaches, spanning manual workflows, health-system-embedded digital tools, and emerging artificial inte...
Machine-learning-based interatomic potentials are widely employed in atomistic simulations, but they struggle to capture long-range electrostatic correlations, which are ubiquitous in polar and biomolecular systems. We present a physics-informed machine-learning interatomic potential that incorporates long-range electrostatic interactions through a polarizable framework. Our model combines two equ...
BACKGROUND: Predicting health insurance uptake remains a critical challenge for policymakers and insurance providers seeking to optimise coverage stra...
Reference libraries of tandem mass spectra (MS/MS) are widely used for metabolite identification in untargeted metabolomics and to train machine-learn...
BACKGROUND: Delayed admission to the intensive care unit (ICU) after trauma can lead to tripling of in-hospital mortality. Accurate ICU resource predi...
Self-organization through noisy interactions is ubiquitous across physics, mathematics, and machine learning, yet how long-range structure emerges fro...
BACKGROUND: Patient recruitment for clinical trials remains a major challenge, with 86% of trials failing to meet enrollment targets on time. In over ...
BACKGROUND: Early Parkinson's disease (PD) presents with subtle symptoms and lacks specific diagnostic methods. Clinical diagnosis primarily relies on...
BACKGROUND: Traditional patient education often lacks personalization and engagement, potentially limiting knowledge acquisition and treatment adheren...
Accurately capturing long-range interactions is critical for molecular dynamics simulations based on machine learning interatomic potentials. We recen...
BACKGROUND: Global inequities in access to cancer diagnostics and treatment contribute to wide variation in cancer mortality-to-incidence ratios (MIRs...
Massively parallel genetic screens have been used to map sequence-to-function relationships for a variety of genetic elements1-5. However, as these ap...
We have trained and externally validated a knowledge-based planning model for radiation therapy planning in the setting of high-grade glioma. Model pe...
Long-tailed data is ubiquitous in real-world applications, posing significant challenges due to imbalanced class distribution and high levels of label...
BACKGROUND: This study presents the design, development, and field evaluation of Vabot, an artificial intelligence (AI)-powered, fully electric autono...
OBJECTIVES: Generative AI chatbots are revolutionizing health education by making complex information more accessible to the public. However, their us...
Adsorbate-adsorbate interaction serves as a critical bridge between macroscopic mass transfer and microscopic adsorption-reaction kinetics in heteroge...
BACKGROUND AND OBJECTIVE: This study introduces the liver cancer segmentator (LCS), a deep learning model designed for automatic and robust segmentati...
Street-level imagery (SLI) is increasingly used in urban analytics for tasks like estimating greenery, conducting transport audits, and assessing faca...
Reproductive performance affects the profitability of a dairy herd. The ability to understand the reproductive capabilities of individual cows and the...