Latest AI and machine learning research in clinical trials for healthcare professionals.
Artificial intelligence (AI)-based screening tools show promise for early identification of chronic liver disease (CLD), yet their effectiveness in real-world settings may depend on clinician response to AI-generated recommendations. We performed a post hoc analysis of the intervention arm of the pragmatic, cluster-randomized DULCE trial, in which primary care clinicians received electrocardiogram...
The optimal Petrov-Galerkin formulation to solve partial differential equations (PDEs) recovers the best approximation in a specified finite-dimensional (trial) space with respect to a suitable norm. However, the recovery of this optimal solution is contingent on being able to construct the optimal weighting functions associated with the trial basis. While explicit constructions are available for ...
BACKGROUND: Clinical decision algorithms used by clinicians guide evidence-based decisions and actions. Automated tools can help with the adoption and...
BACKGROUND: Patients with cancer are at elevated risk of venous thromboembolism (VTE). While primary thromboprophylaxis reduces VTE incidence, it also...
BACKGROUND: Hemoglobin A1C (HbA1C) is the gold standard for assessing long-term glycemic control in people with diabetes. Increasing use of continuous...
The probiotics field, a historically popular yet scientifically debated discipline, is moving beyond a decades-long promotion of 'first-generation' fo...
BACKGROUND: Digital health tools, particularly patient portals, can support caregiving, but there is limited understanding of how sociodemographic and...
BACKGROUND: Operating room nurses (ORNs) are at high risk for compassion fatigue (CF), which significantly impairs individuals' well-being, undermines...
Ligand-based drug design (LBDD) has long driven therapeutic innovation; however, its traditional potency-centered paradigm often oversimplifies biolog...
Road traffic accidents cause substantial fatalities and economic losses, yet large-scale severity analysis is often constrained by limited access to h...
In this article, we present a novel off-policy, safe reinforcement learning (RL) approach for nonlinear dynamical systems under input saturation that ...
BACKGROUND: Over the past decade, neuropsychopharmacology has shifted from stagnation to momentum, with first-in-class mechanisms and biomarker-enable...
This study employs Physics-Informed Neural Networks (PINNs) to simulate the thermal dynamics of biological tissue under laser irradiation by embedding...
Artificial intelligence (AI) is increasingly used for diagnostic screening. In diabetic retinopathy screening, autonomous AI systems can identify dise...
OBJECTIVES: To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage...
BACKGROUND: Investments in health artificial intelligence (AI) are accelerating across European Union member states, yet evidence linking national AI ...
The growing demand for safe, sustainable, and clean-label food preservation strategies has accelerated interest in plant-derived essential oils (EOs) ...
BACKGROUND: Proper needle visualization is a major technical challenge for novices learning ultrasound-guided regional anesthesia (UGRA). We developed...
BACKGROUND: The integration of artificial intelligence (AI) into reproductive medicine and gynecologic oncology has driven transformative advances in ...
Background:The enhancement of the therapeutic window (TW) in oncology remains a significant challenge, as the majority of anticancer treatments face d...