Domestic Violence

Predicting Outcomes of Traumatic Brain Injury Using Machine Learning Models Among Patients at Kilimanjaro Christian Medical Centre, Tanzania: A Registry-Based Cohort Study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health burden, disproportionately affecting low- and middle-income countries (LMICs) where access to neurocritical care is limited. Accurate and context-appropriate prognostic models are crucial to guide early clinical decision-making and optimize resource allocation in such settings. This study aims to develop and evaluate machine le...

AI-assisted post-mortem classification of vehicle trauma in free-roaming cats using transformer-based large language models.

Veterinary forensic science is a specialized discipline that investigates non-natural animal deaths and injuries. Determining the cause of death in free-roaming cats often presents challenges due to underdeveloped pattern recognition methods and the intricate interplay of events and contributing factors. The integration of natural language processing into forensic investigations may offer potentia...

Mapping the literature on improving primary-to-secondary care referrals through the lens of the Quintuple Aim framework for healthcare improvement: a scoping review.

OBJECTIVES: To explore and map the literature on referral pathway gaps and improvement strategies in primary healthcare (PHC) to secondary healthcare ...

Impact of guideline-based prompting on the large language model performance in dental trauma management clinical decision-making.

This study evaluated the impact of guideline-based prompting on the performance of large language models (LLMs) in answering dental trauma-related que...

Establishing diagnostic thresholds for adult ADHD screening in taxi drivers: validation of the CAARS-S via Item Response Theory and Machine Learning.

BACKGROUND: Adult Attention-Deficit/Hyperactivity Disorder (ADHD) is under-recognized in professional drivers, yet it poses significant safety risks. ...

Performance of federated learning models in health services research: A systematic review and meta-analysis.

PURPOSE: This study aimed to assess the performance of federated learning (FL) models and compare their performance with local and centralized models....

Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology A secondary analysis of the AI-PEDURO online repository.

INTRODUCTION: While an increasing number of artificial intelligence (AI) models are being developed in pediatric urology, the extent of race/ethnicity...

Accuracy of portable artificial intelligence-supported fundus camera in the screening of diabetic retinopathy in primary care.

OBJECTIVE: Research on the use of portable fundus cameras utilizing artificial intelligence (AI) for diabetic retinopathy (DR) screening in primary ca...

Contrastive Learning-Powered Ultrafast Genome-Wide Virtual Screening: The Rise of DrugCLIP.

Virtual screening (VS) stands as a cornerstone of early-stage drug discovery, yet long-standing hurdles-including prohibitive computational cost and l...

From Shadows to Signals: Exploring the Strengths and Limitations of AI in Breast Cancer Lesion Detection on Screening Mammograms Compared to Radiologists and Radiology Trainees.

OBJECTIVES: Breast cancer screening with mammography reduces mortality, but interpretation errors persist. While artificial intelligence (AI) shows pr...

An Effective Machine Learning for Prioritizing Hearing Loss Screening in Older Adults: A Primary Care Approach.

BACKGROUND/OBJECTIVES: The high prevalence of hearing impairment in older adults and the scarcity of diagnostic resources in primary health care (PHC)...

Baseline biochemical evaluation for etiologic differentiation in ACTH-dependent Cushing's syndrome: machine learning and composite score analysis in a multicenter cohort of 566 patients.

BACKGROUND: Limited availability of corticotropin-releasing hormone (CRH) currently complicates the differentiation of adrenocorticotropin (ACTH)-depe...

Predicting stroke risk using retinal imaging with artificial intelligence: a scoping review of current evidence.

BACKGROUND: Stroke remains a leading cause of mortality and long-term disability worldwide, while currently used clinical risk prediction tools demons...

From Data to Early Diagnosis: Artificial Intelligence as a Tool to Support Screening and Detection of Autism Spectrum Disorder in Childhood.

PURPOSE OF REVIEW: Autism spectrum disorder (ASD) is a neurodevelopmental condition with significant implications for childhood development and public...

Toward a Structured Imaging Reporting System for Kidney Masses: The Molecular Imaging Perspective on KI-RADS.

Recently, a structured approach to renal mass characterization known as the Kidney Imaging Reporting and Data System (KI-RADS) was proposed. In that p...

Machine Learning Frameworks for Wearable-Based Stress Modeling in Naturalistic Settings: Scoping Review.

BACKGROUND: Stress, as commonly recognized, is an integral part of modern life and can significantly affect both mental and physical health. While sub...

Impact of Large Language Model-Based AI Tools on Physician-Patient Communication: Systematic Review and Meta-Analysis.

BACKGROUND: Recent advances in large language models (LLMs) such as GPT-3/4 have spurred the development of artificial intelligence (AI) chatbots and ...

Accuracy of Machine Learning Algorithms Based on Electroencephalogram in Sleep Apnea Detection: Systematic Review and Meta-Analysis.

BACKGROUND: Sleep apnea (SA) is a serious sleep disorder, and its diagnostic gold standard, polysomnography, is costly and time-consuming. Electroence...

Incomplete Reporting Persists in Orthopedic Machine Learning Models: A Systematic Review of TRIPOD and TRIPOD+AI Reporting Completeness.

BACKGROUND AND OBJECTIVE: Machine learning (ML) prognostic models in orthopedic surgery are published at an accelerating pace, yet whether reporting m...

Artificial Intelligence Based Ultrasound Screening for Antenatal Detection of Placenta Accreta Spectrum.

BACKGROUND: Placenta Accreta Spectrum is a leading cause of maternal morbidity and mortality and is increasing in incidence, yet only 30-50% of cases ...