Surveillance

Operational mechanisms and application advances in artificial olfactory systems.

Artificial olfactory systems represent biomimetic platforms that emulate biological olfaction for volatile compound detection and discrimination. Biological olfaction achieves efficient perception through the specific binding of volatile molecules to olfactory receptors (OR), odorant-binding proteins (OBPs), and associated chemosensory proteins, followed by neural encoding, providing a theoretical...

Artificial intelligence for left ventricular strain.

PURPOSE OF REVIEW: To review recent advances in artificial intelligence (AI) for left ventricular (LV) strain echocardiography, with emphasis on studies published during the preceding 18 months, and to assess current evidence for measurement performance, workflow integration, emerging AI-based approaches, disease-specific applications and barriers to widespread clinical adoption. RECENT FINDINGS: ...

Using large language models for automated assessment of reporting quality and completeness of prediction model studies.

BACKGROUND AND OBJECTIVES: Transparent and complete reporting in scientific papers is important for interpretation of study results and for downstream...

Development and validation of a rule-based tool for quality management reporting in a genetics laboratory.

INTRODUCTION: Quality management systems are essential in clinical laboratories to ensure optimal operational output. However, report generation still...

Global Trends and Research Hotspots in Soccer Injury Literature: A Bibliometric Analysis (1904-2025).

IMPORTANCE: Soccer is associated with a substantial global injury burden, particularly involving lower-extremity and head injuries. Despite extensive ...

Evaluating the Methodological Quality of Artificial Intelligence-Assisted Systematic Reviews: Protocol for a Mixed Methods Meta-Research Study.

BACKGROUND: Artificial intelligence (AI), including large language models (LLMs), is increasingly integrated into systematic review (SR) workflows. AI...

Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence.

BACKGROUND: Machine learning (ML) and deep learning (DL) show promise for fall risk prediction, but prior reviews focused mainly on real-time fall det...

Cardiovascular Disease Events and Life Expectancy Lost Attributable to Machine Learning-Derived Dietary Networks: Evidence from Canadian National Nutrition Survey Linked to Routinely Collected Administrative Databases.

BACKGROUND: Artificial intelligence and machine learning (ML) are transforming nutritional epidemiology by revealing dietary network structures invisi...

Machine learning models for outcome prediction of patients with ischaemic stroke undergoing reperfusion therapy: a systematic review and meta-analysis.

BACKGROUND: Reperfusion therapy, including thrombolysis and thrombectomy, is crucial for ischaemic stroke treatment. However, patient outcomes often r...

A two-stage OOD-aware approach for mosquito species detection based on RT-DETRv2.

Mosquitoes are important vectors of infectious diseases, and accurate species identification is essential for effective surveillance and control. Trad...

CT-based interpretable delta-radiomics model for risk stratification of pulmonary ground-glass nodules: a multicentre study.

OBJECTIVES: To develop and externally validate an interpretable fusion model combining multi-time-point CT radiomics with clinical-semantic features t...

Optimising supervised machine learning algorithms predicting cigarette cravings and lapses for a smoking cessation just-in-time adaptive intervention (JITAI).

This study aimed to optimise the balance between participant burden and algorithm performance for predicting high-risk moments in a smoking cessation ...

EXPRESS: Artificial Intelligence in Diabetes Care: Toward Precision Diagnosis and Personalized Management.

AimsDiabetes mellitus is a global health challenge requiring innovative solutions for early diagnosis, personalized treatment, and ongoing management....

Global Adoption and Readiness for Artificial Intelligence (AI) and Digital Health Technologies in Infection Prevention and Control (IPC): A WHO Global Survey.

BACKGROUND: Artificial intelligence and digital health technologies may strengthen Infection Prevention and Control through enhanced surveillance, dec...

Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.

To systematically evaluate the diagnostic accuracy of artificial intelligence (AI) models in periapical radiography for detection, classification, and...

Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review.

BACKGROUND: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominant...

An Intelligent Photocatalytic Self-Cleaning SERS Platform with Machine Learning for On-Site Detection of Multiple Antibiotics.

The widespread use of antibiotics in aquaculture has led to persistent residues in aquatic environments, necessitating the development of sensitive, s...

Comparative analysis of traditional and deep learning time series architectures for influenza A infectious disease forecasting.

Influenza A remains a major cause of respiratory mortality worldwide, motivating accurate forecasting to support timely preparedness and resource allo...

The Asian Pacific Association of the Study of the Liver expert survey on artificial intelligence-assisted reporting of liver histopathology in metabolic dysfunction associated fatty liver disease.

INTRODUCTION: Artificial intelligence (AI) and digital pathology have the potential to augment liver biopsy interpretation in MAFLD in clinical practi...

Trustworthy artificial intelligence in predictive medicine: cancer survival analysis using ethical-by-design and explainable artificial intelligence models.

OBJECTIVES: This study aims to develop and evaluate a trustworthy and ethical-by-design machine learning (ML) framework for predicting 5-year cancer s...