Surveillance

Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery.

Antimicrobial resistance (AMR) is an intractable problem that has the potential to significantly limit advances in human health. Recently, the UN General Assembly (UNGA) High-Level Statement on AMR defined targets for addressing the impact of resistance on human, animal, and environmental health. For human health, the discovery and development of new antibiotics, antimicrobial stewardship programm...

Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.

Body dysmorphic disorder (BDD) is characterized by distressing preoccupations with perceived appearance flaws, leading to functional impairment and suicidal ideation (SI). Traditional approaches for monitoring clinical deterioration in BDD include self-reports and clinician assessments, which can miss acute changes in risk due to infrequent administration and recall biases. Alternatively, real-tim...

Off-the-Shelf Large Language Models for Guiding Pharmacoepidemiological Study Design.

This study aimed to assess the ability of two off-the-shelf large language models, ChatGPT and Gemini, to support the design of pharmacoepidemiologica...

Beyond black boxes: using explainable causal artificial intelligence to separate signal from noise in pharmacovigilance.

Artificial intelligence (AI), particularly machine learning (ML), is increasingly influencing pharmacovigilance (PV) by improving case triage and sign...

Acceptability and feasibility of a new-generation alcohol biosensor: A mixed methods evaluation in a large community sample.

Wearable alcohol biosensors offer an innovative solution for real-time alcohol monitoring, yet concerns about comfort, privacy, and social acceptabili...

Preserving Scientific Integrity in Academic Publishing: Navigating Artificial Intelligence, Journal Policies, and the Impact Factor as a Quality Indicator.

The integration of artificial intelligence (AI), the rise of mega-journals, and the manipulation of impact factors present challenges to scientific in...

Screening for Barrett Esophagus and Esophageal Adenocarcinoma: Approaches and Outcomes.

Barrett esophagus (BE) is the only known histological precursor to esophageal adenocarcinoma (EAC). The incidence of EAC has risen significantly over ...

Prediction of incident atrial fibrillation: A comprehensive evaluation of conventional and artificial intelligence-enhanced approaches.

BACKGROUND: Multiple risk scores and biomarkers have been proposed for the prediction of atrial fibrillation (AF), but it is unknown how these compare...

Navigating Medical Device Safety: Current Status, Challenges, and Future Regulatory Directions.

Medical devices are indispensable in modern healthcare. They enable the prevention, diagnosis, and treatment of diseases while enhancing patient outco...

Machine Learning in Venous Thromboembolism-Why and What Next?

Venous thromboembolism (VTE) remains a leading cause of cardiovascular morbidity and mortality, despite advances in imaging and anticoagulation. VTE a...

Classifying Care Needs and Determining Nursing Diagnoses of Older Patients With Cardiometabolic Multimorbidity.

AIM: This study aimed to gain insight into the thoughts, perceptions and needs of nurses caring for older adults with cardiometabolic multimorbidity r...

Child sex doll and sex robot research: Taking a child rights perspective.

Direct child sexual abuse is pervasive, with consistent estimates across reviews and epidemiology at up to 20 % for girls and up to 15 % for boys; the...

How do experts classify sepsis cases for sepsis surveillance? Lessons learned from a Behavioural Artificial Intelligence Technology (BAIT) approach.

OBJECTIVES: To identify relevant objective variables for retrospective identification of 'suspected infection' and sepsis, using behavioural artificia...

AI-driven diagnosis of Lassa fever: Evidence from Nigerian clinical records.

BACKGROUND: Neglected Tropical Diseases (NTDs), particularly Lassa fever, remain a significant public health challenge in Nigeria, often presenting wi...

Leveraging machine learning for predicting Opisthorchis viverrini infection in cats: A tool for veterinary epidemiology.

Opisthorchis viverrini (Ov) infection is a major public health concern in the Greater Mekong Subregion, with cats as key reservoir hosts. Although mac...

Intraductal Papillary Mucinous Neoplasm and Pancreatic Cancer: Opportunity Knocks Twice.

Pancreatic cystic lesions are widely recognized as harbingers of pancreatic cancer. Intraductal papillary mucinous neoplasm (IPMN) is the most common ...

Computer-aided diagnosis of DDH using ultrasound: deep learning for segmentation and accurate angle measurement aligned with radiologist's clinical workflow.

AIMS: A computer-aided diagnosis (CAD) system for automated evaluation of developmental dysplasia of the hip (DDH) via ultrasound, integrating Deep Le...

Machine Learning Risk Stratification for Older Breast Cancer Survivors: Clinical Care Implications.

OBJECTIVE: To develop and validate a clinical risk prediction algorithm to identify breast cancer survivors at high risk for adverse outcomes. STUDY S...

Evaluating predictive performance and generalizability of traditional and artificial intelligence models in predicting surgical site infections postspinal surgery: a systematic review.

BACKGROUND CONTEXT: Surgical site infections (SSIs) are a significant complication following spinal surgery. These infections contribute to increased ...

Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions for via metagenomic sequencing.

Antimicrobial resistance (AMR) represents a critical global health challenge, demanding rapid and accurate antimicrobial susceptibility testing (AST) ...