Domestic Violence

Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.

Artificial intelligence (AI) has emerged as a transformative tool for improving the detection, prediction, and prevention of adverse drug reactions (ADRs) in cardiovascular (CV) medicine, a domain characterized by high drug utilization, complex multimorbidity, and substantial polypharmacy. Traditional pharmacovigilance (PV) systems, particularly spontaneous reporting, remain limited by underreport...

Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.

BACKGROUND: Standard echocardiography reports use complex terminology, limiting patient comprehension and exacerbating preconsultation anxiety. Large language models (LLMs) can transform technical data into patient-friendly narratives by incorporating longitudinal comparisons with prior examinations. OBJECTIVE: This study aims to develop an LLM-based patient-friendly echocardiography reporting sys...

The Rise of AI Companions: From Prediction to Surgery in the Digital Twin Era.

Artificial intelligence (AI) is rapidly reshaping orthopaedic surgery, supported by advances in data science, computational power, and perioperative d...

Awareness, knowledge and attitudes towards artificial intelligence among nursing students: a systematic review protocol.

INTRODUCTION: Internationally, nursing students' awareness and familiarity with artificial intelligence (AI) remain a challenge as evidenced by the cu...

Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.

TOPIC: Artificial intelligence (AI) is increasingly applied to support decision-making in ophthalmology. This review evaluates the ability of AI to pr...

BiCervi: Pap smear microscopy image dataset for cervical cancer detection and classification.

Cervical cancer is still a major public-health challenge, especially in low and middle-income countries where there are limited numbers of specialists...

Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.

BACKGROUND: Large language models (LLMs) are increasingly embedded in conversational agents for cardiometabolic care. These systems could support self...

Smartphone Applications in Dermatology With Special Focus on AI: A European-Continent-Based Review.

Smartphone applications for dermatology are widely available across Europe, yet evidence on their characteristics, validation, and regulatory complian...

Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.

BACKGROUND: Depressive symptoms are common yet often underrecognized in routine care, underscoring the need for scalable screening approaches beyond e...

A Comparative Study of the Psychometrics and Feasibility of Artificial Intelligence Algorithms Versus the Standard Screening Methods for Oropharyngeal Dysphagia in Older Adults: A Scoping Review.

Oropharyngeal dysphagia (OD) is highly prevalent (35.6%-47.4%) in hospitalized older patients but clinical screening is slow and labor-intensive, lead...

Augmenting Medical Judgment in Signal Confirmation: Design Considerations for an AI-Enabled Causality Assessment Framework.

The safety signal assessment process evaluates the potential causal association between a medicinal product and a specific adverse event by integratin...

Bouncing back from stress: objective markers of expressive flexibility and resilience in emergency healthcare workers using computer vision.

Healthcare workers (HCWs) in emergency departments face significant mental health risk due to chronic stressors and repeated trauma, yet symptom under...

Radiographers' accuracy in interpreting acute musculoskeletal X-rays when supported by artificial intelligence - A multi-site cross-sectional study.

INTRODUCTION: The artificial intelligence (AI) tool BoneView™ has been introduced into clinical practice to support skeletal X-ray interpretation. Thi...

The Role of Artificial Intelligence in Equine Colic: A Scoping Review of Diagnostic, Prognostic, and Decision-Support Applications.

Equine colic remains one of the leading causes of mortality in horses, with timely diagnosis and accurate prognostic assessment being critical for cli...

The Use of Generative Artificial Intelligence in Systematic Literature Reviews: A Rapid Review of the Literature.

OBJECTIVES: Systematic literature reviews (SLRs) underpin life sciences research but are resource intensive. Generative artificial intelligence, parti...

AI-based organ weight estimation from postmortem computed tomography.

Accurate organ weight determination is essential in forensic autopsy. Postmortem computed tomography (CT) combined with Artificial Intelligence (AI)-b...

Role of artificial intelligence in developing predictive models for major adverse cardiovascular outcomes using CCTA adipose tissue characteristics: a systematic review and meta-analysis.

Cardiovascular diseases remain as a leading cause of mortality and morbidity worldwide, with coronary artery disease (CAD) and its complications, coll...

Neurobiosensory and Visual Patterns of Attentional and Emotional Functioning in Children: A Machine Learning Approach.

BACKGROUND: Attentional functioning in childhood emerges from the interaction between cognitive, behavioral, and emotional processes. Children with th...

An AI-Assisted Tool to Predict Continuous Glucose Monitor Adherence in Children With Type 1 Diabetes in Oman: Protocol for a Multiphase Mixed Methods Translational Study.

BACKGROUND: Type 1 diabetes mellitus (T1DM) in children requires sustained self-management to achieve glycemic targets. Continuous glucose monitoring ...

Machine learning modeling to predict HCC locations in cirrhotic patients undergoing MRI - a proof-of-concept study.

PURPOSE: Current hepatocellular carcinoma (HCC) surveillance guidelines rely on manually defined LI-RADS (Liver Imaging Reporting and Data System) fea...