Identifying and Reporting Dependent Adult abuse

An evidence informed framework for artificial intelligence in rare breast cancers using small cohort validation synthetic data practices and clinical governance.

Rare breast cancers represent a clinically important but underrepresented group of malignancies. In this Perspective, rare breast cancers are considered within the broader rare cancer definition of an annual incidence below 6 cases per 100,000 persons, while also recognizing breast-specific rarity based on uncommon histology, molecular hallmarks, clinical presentation or sex-specific occurrence. T...

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 breast cancer from demonstrated real-world clinical utility. Most systems reporting accuracy above 95% on curated datasets have not been externally validated across diverse populations and imaging platforms. PURPOSE: We critically examine AI-driven di...

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...

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...

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....

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 (A...

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 ...

AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review.

BACKGROUND: Pharmacovigilance aims to protect patient safety by identifying and managing adverse events associated with pharmaceuticals. Determining t...

Automated detection of trigeminal neuralgia using multi-domain EEG feature analysis and CNN-attention architecture: a comparative machine learning study.

Trigeminal neuralgia (TN) is a debilitating neuropathic pain disorder characterized by sudden, intense facial pain, with diagnosis heavily reliant on ...

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...

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...

GATESynergy: Integrating Molecular Global-Local Aggregator and Hierarchical Gene-Gated Encoder for Drug Synergy Prediction.

Combination drug therapy is an effective approach to combating drug resistance and enhancing therapeutic efficacy in complex diseases such as cancer. ...

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...

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...

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 ...

Predicting Neonatal Respiratory Outcomes Using Machine Learning: A Systematic Review Through a Life Course Lens.

BACKGROUND: Neonatal respiratory outcomes remain leading drivers of neonatal intensive care unit (NICU) morbidity, mortality, and prolonged hospitaliz...

Towards transparent and reliable AI in gastroenterology: pragmatic recommendations for evaluation and reporting.

Artificial intelligence (AI) is expanding in gastroenterology, particularly in endoscopy and imaging, where models support detection, classification, ...

A systematic map of generative AI guidelines and reporting in ecology and evolutionary biology: towards the framework of AI disclosure for Improved Transparency (AIdIT).

Generative artificial intelligence (AI) is rapidly becoming embedded across scientific workflows, yet mechanisms for transparently documenting its use...

Technology-enabled telerehabilitation for Parkinson's disease: a scoping review of digital rehabilitation systems, delivery architectures, and implementation challenges.

INTRODUCTION: Digital technologies are increasingly integrated into neurorehabilitation programs for Parkinson's Disease (PD), enabling remote deliver...

Applications of traditional machine learning and deep learning algorithms in obesity prediction or classification: a systematic review of comparative performance.

This systematic review evaluated traditional machine learning (TML) and deep learning (DL) approaches for obesity prediction in longitudinal studies a...