Surveillance

Integrating human and artificial intelligence for robust postmarketing safety surveillance systems: reflections from the FDA Sentinel Innovation Center.

OBJECTIVES: To describe considerations for integration of human and artificial intelligence for creating a postmarketing surveillance system capable of timely and reliably identifying causal effects of medications on safety endpoints. MATERIALS AND METHODS: The FDA has prioritized more extensive Electronic Health Records (EHR) integration along with generative artificial intelligence and machine l...

User Acceptability and Adoption of AI-Generated Lifestyle Intervention Recommendations: Scoping Review and Theoretical Integration.

BACKGROUND: Artificial intelligence (AI)-generated lifestyle recommendations are increasingly used to support health behavior change. However, AI advice does not necessarily mean that users will accept or adopt those recommendations. Although prior reviews have examined AI-enabled lifestyle interventions and health behavior technologies, fewer have focused on whether users accept and adopt AI-gene...

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...

Machine learning-based prognosis and early death prediction in de novo stage IV breast cancer patients with bone metastasis: a SEER database and multicentre retrospective study.

Bone is the most common site of distant metastasis in breast cancer (BC), and the development of bone metastasis (BM) is associated with reduced survi...

Artificial Intelligence in Clinical Genetics: Current Applications and Challenges.

Multiomics, next-generation, and long-read sequencing approaches have transformed the practice of medical genetics. Complex cases often require severa...

Tree-based machine learning models recover rule-based flare labels from virtual symptom diaries in pediatric Behçet's disease: an in silico proof-of-concept study.

Behçet's disease (BD) in childhood is characterised by recurrent inflammatory flares that can result in significant morbidity, most notably with ocula...

Recent advances in machine learning and Bayesian modeling for tropical disease prediction, diagnosis, and risk analysis: a scoping review.

BACKGROUND: Enhancing the capacity to forecast tropical disease transmission, identify key risk factors, and support timely public health responses is...

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...

Transforming Pulmonary Arterial Hypertension: Key Milestones and Future Perspectives.

Pulmonary arterial hypertension (PAH) is a rare, progressive disease of the precapillary pulmonary arteries, characterized by fibroproliferative vascu...

[Transformation of Epidemiology in the Age of Artificial Intelligence].

Epidemiology has been fundamental for analyzing health problems and supporting decision-making in healthcare systems and public health. However, tradi...

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...

SymBOL: A General-Purpose Symbolic Learner for Scientific Discovery Using Bayesian Optimization-Enhanced Large Language Models.

Symbolic Regression (SR) is a core challenge in both physics and artificial intelligence, aiming to identify mathematical equations from experimental ...

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...

AI-Assisted Chest X-Ray Interpretation in Resource-Limited Settings: LuAna Stepped-Wedge Trial Protocol.

BACKGROUND: Artificial intelligence (AI) has the potential to transform chest radiography interpretation by enhancing diagnostic accuracy, identifying...

Author Correction: The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.

Prediction of Exercise Intolerance in the Recovery Period After Cardiovascular Surgery Using Perioperative Clinical Parameters.

OBJECTIVE: To develop and validate a machine learning model for predicting reduced exercise capacity at 3 months postoperatively using clinical parame...

[Chinese guideline for the diagnosis and treatment of hospital-acquired pneumonia and ventilator-associated pneumonia in adults (2026 edition): protocol].

This document serves as the protocol for the development of the Chinese Guideline for the Diagnosis and Treatment of Hospital-Acquired Pneumonia and V...

Disparities in severe infection risk associated with systemic lupus erythematosus: evidence from a causal forest analysis.

OBJECTIVES: Severe infections are a primary cause of morbidity and premature mortality in patients with Systemic Lupus Erythematosus (SLE). Although S...

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, ...