Prescriptions

Clinical Risk Management and Patient-safety Research: A Large-scale Bibliometric Mapping of the Field and Its Emerging Directions (Master vs. 2021-2025).

BACKGROUND: To map the evolution, knowledge structure, and emerging directions of clinical risk management (CRM) and patient-safety research by contrasting a full-timespan corpus with the most recent 5-year subset. METHODS: Records were retrieved from Web of Science Core Collection (n=23,498; 1986-2026) and Scopus (n=51,710; 1958-2026) using harmonized queries targeting patient safety, adverse eve...

A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.

Domain-specific evaluation is essential for clinical validation. We propose S.C.O.R.E. (Safety, Consensus & Context, Objectivity, Reproducibility, Explainability), a five-dimensional framework for structured expert evaluation of LLM-generated healthcare responses. S.C.O.R.E. has been validated against quantitative metrics (BLEU, ROUGE, and BERTScore) using three LLMs (GPT-4o, Claude 4 Sonnet, and ...

[The research progress and outlook on the effects of different exercise modalities on working memory function in middle-aged and elderly individuals].

This review provides a comprehensive analysis of the effects of different exercise modalities on working memory function in middle-aged and older adul...

Accuracy of the rapid-response electroencephalography's Automated Seizure Burden Estimator: A follow-up validation study of version 8 (AccuRASE II).

OBJECTIVE: Ceribell Inc.'s point-of-care electroencephalographic (EEG) system and artificial intelligence-based Automated Seizure Burden Estimator (AS...

Predicting SIRS after PCNL using machine learning: the joint impact of sarcopenia and staghorn stones.

PURPOSE: To develop and validate machine learning models for predicting systemic inflammatory response syndrome (SIRS) after percutaneous nephrolithot...

An integrated evaluation framework for synthetic clinical data in severely imbalanced settings: fidelity, privacy-risk profiling, and diagnostic utility.

BACKGROUND: The development of clinical artificial intelligence models is constrained by limited access to high-quality electronic health record data,...

Integrative transcriptomic and CRISPR dependency analysis identifies hepatoblastoma-specific essential genes and actionable vulnerabilities.

BACKGROUND: Hepatoblastoma (HB) is the most common primary liver malignancy in childhood, yet its molecular determinants, functional dependencies, and...

Evaluation of an AI-based segmentation model for mandibular fracture segmentation on CBCT using volumetric and 2D slice-based validation approaches.

OBJECTIVE: This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based segmentation model for mandibular fracture...

Enhancing Cognitive Functions of Older Adults With Software Robot: Longitudinal Exploratory Field Study.

BACKGROUND: The global prevalence of dementia continues to rise and demands scalable, nonpharmacological interventions. Digital cognitive training has...

Beyond Area Under the Receiver Operating Characteristic Curve: Evaluating Predictive Performance Metrics Under Class Imbalance in Real-World Clinical Data.

BACKGROUND: Predictive models increasingly support clinical decision-making, although imbalanced outcome distributions are common in health care datas...

Coproduction Without Youth? Closing the Participation Gap in Digital Mental Health Research.

Young people are among the most intensive users of digital and generative artificial intelligence (GenAI)-enabled mental health tools, yet they remain...

HCCD-DS v2: a transparent synthetic benchmark for human-AI decision support under contextual uncertainty.

Decision support pipelines increasingly combine machine learning predictions with human judgment, yet most public benchmarks evaluate model outputs on...

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Structure-based virtual screening (VS) via molecular docking is a pivotal approach for hit identification. Many artificial intelligence (AI)-powered p...

Advanced machine learning-based screening for primary aldosteronism with plasma steroids, potassium, and renin.

Commonly used screening tests for primary aldosteronism (PA) provide suboptimal diagnostic accuracy, particularly with antihypertensive medication use...

Dual channel drug-drug interactions extraction based on cross attention.

Drug-drug interactions (DDIs) play a critical role in several biomedical applications, particularly in pharmacovigilance. While neural networks have s...

Unit dose drug dispensing systems in hospitals: a systematic review of medication error reduction and cost-effectiveness.

BACKGROUND: Medical errors pose significant risks to patient safety and public health. Automated unit dose drug dispensing systems (UDDSs) have emerge...

Network analysis reveals that bHLH transcription factors positively regulate rice resistance to Magnaporthe oryzae.

Transcription factors (TFs) and nucleotide-binding leucine-rich repeat (NLR) genes are core components of the immune response in rice against Magnapor...

Multimodal machine learning and deep graph neural networks for the prediction of molecular inhibitory activity and disease associations.

Effectively integrating a variety of biological and chemical data sources is essential for the acceleration of drug discovery and precision diagnostic...

Early prediction and risk assessment of adverse drug combinations using ensemble learning.

Modern day healthcare has seen an increase in polypharmacy, which is the prescription of multiple drugs as medication to treat illnesses simultaneousl...

A Two-Stage, Semi-Supervised Deep Learning Framework for the Detection and Classification of Ambient Pollen using Evanescent Wave Scattering.

Airborne pollen, an aeroallergen, impacts human health, particularly as climate change shifts pollen dynamics, requiring accurate monitoring to suppor...