Domestic Violence

Quantifying Artificial Intelligence Contribution in Academic Writing: Development of the Transparency and Reporting of Artificial Intelligence Contribution for Evaluating Submissions Instrument.

BACKGROUND: There has been an increase in the availability and use of artificial intelligence (AI) across many professional domains, and there are traces of AI in nearly every manuscript published using modern technology. It is becoming increasingly difficult for peer reviewers, editorial teams, and journal readers to identify the degree to which authors have used AI in the development of their ma...

A comprehensive evaluation of seven osteoporosis screening tools.

UNLABELLED: This review evaluates the sensitivity, specificity, and predictive values of seven osteoporosis screening tools in populations such as postmenopausal women, older adults, and patients with specific diseases. These tools offer advantages, including high sensitivity (facilitating early detection of osteoporosis), high cost-effectiveness, and the ability to be customized according to dise...

Artificial intelligence for pediatric rare disease diagnosis: a multimethod study integrating published evidence and clinician interviews.

BACKGROUND: Pediatric rare diseases are highly heterogeneous and are frequently associated with missed or delayed diagnosis, creating substantial burd...

Language-dependent performance variation in large language models for dental trauma management: a comparative evaluation of ChatGPT-5.2, Gemini 3.0, and Claude 4.5 Sonnet.

BACKGROUND: Large language models (LLMs) are increasingly evaluated for medical question answering and clinical information tasks, yet the impact of q...

DeepHeptox: An Interpretable Deep Learning Model for Multi-Endpoint Hepatotoxicity Prediction of Chemical Compounds.

Hepatotoxicity represents a major adverse outcome of chemical exposure, as the liver plays a central role in xenobiotic metabolism and detoxification....

Translational gaps and clinical readiness of artificial intelligence and multimodal imaging in breast cancer diagnostics.

BACKGROUND: A persistent translational gap separates the high research-benchmark performance of artificial intelligence (AI) and advanced imaging in b...

Regenerable "Two-in-One" molecularly imprinted nanozymes colorimetric sensor array combined with deep learning enables selective removing and visual discriminate of tetracycline antibiotics.

Tetracycline (TC) and its analogues are widely employed broad-spectrum antibiotics. Their abuse and environmental residues have raised severe ecologic...

A scoping review on artificial intelligence-based tools for cardiovascular disease risk prediction.

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of death worldwide, making early risk prediction essential for improving outcomes. Althoug...

Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.

Cystathionine β-synthase (CBS) has emerged as an important therapeutic target implicated in cancer and Down syndrome, yet the discovery of selective C...

Impact of Artificial Intelligence Assistance on Diagnosing Traumatic Pneumothorax: A Comparison of Specialist and Non-Specialist Emergency Physicians.

INTRODUCTION: Supine chest radiography is routinely used in trauma care; however, its sensitivity is limited in pneumothorax detection. Although artif...

Artificial Intelligence Across the Vaccine Clinical Trial Lifecycle: Evidence, Readiness, and Guardrails.

Artificial intelligence (AI) is increasingly being used to support clinical research, but its value in vaccine clinical trials requires careful eviden...

Enhancing the golden hour: classification of traumatic brain injury, severity, and concomitant clinical phenotypes using prehospital continuous physiological data during air transport.

During emergency transport, clinical assessment and vital signs may lack the sensitivity to identify traumatic brain injury (TBI) and identify specifi...

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

BACKGROUND: Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (S...

Current Perspectives on Thyroid Nodule Evaluation: Integrating Cytomorphology, Ultrasound Risk Stratification, and Molecular Testing.

Fine-needle aspiration biopsy (FNAB) remains the cornerstone of preoperative evaluation of thyroid nodules and represents one of the most enduring and...

Human salivary microbiome as a potential non-invasive biomarker for early-onset colorectal cancer screening: a prospective study.

BACKGROUND: The incidence of early-onset colorectal cancer (EOCRC) has been increasing in recent years, the carcinogenesis of which has been linked to...

Techniques, Performance, and Feasibility of Natural Language Processing for Abstract Screening in Evidence Synthesis: A Systematic Review.

BACKGROUND: Natural language processing (NLP) techniques offer promising solutions for semi-automating the time-consuming process of abstract screenin...

Artificial intelligence for short-term post-mortem interval estimation: a systematic review with PROBAST and TRIPOD+AI appraisal.

BACKGROUND: Estimating the post-mortem interval (PMI) is pivotal in forensic casework, yet traditional approaches are imprecise and context-dependent....

Deep-learning CT reconstruction improves image quality but not diagnostic accuracy for sacral fragility fractures: MRI remains essential.

PURPOSE: To determine whether modern deep-learning CT reconstruction improves trauma-relevant diagnostic performance for sacral fragility fractures (S...

A Guideline-Concordant Chatbot Framework for Structured Colorectal Cancer Screening: Multistage Feasibility Study.

BACKGROUND: Colorectal cancer (CRC) screening relies on structured risk assessment and guideline-concordant communication, which remain challenging to...

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

BACKGROUND: Older adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admiss...