Surveillance

Response: On misconceptions in the ethical assessment of medical AI.

The article by Durán and Jongsma attempts to establish philosophical grounds for trust in medical artificial intelligence (AI) by positing concerns about opacity, 'black box' algorithms and epistemic justification. However, their argument conflates distinct technical concepts, mischaracterises both machine learning (ML) and statistical inference and fails to engage with established methodological ...

Disease and Health Surveillance in Companion Animals Using Artificial Intelligence and Machine Learning.

Companion animal disease surveillance now benefits from collated databases of electronic health records and artificial intelligence. This review examines computational approaches for analyzing unstructured veterinary clinical text, from rule-based systems through traditional neural networks to modern transformer models. Domain-adapted encoders like PetBERT enable efficient disease coding and syndr...

Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.

Timely activation of massive hemorrhage protocols (MHP) is critical to prevent exsanguination and improve survival in trauma patients. Current clinica...

The One Health resistome: Integrating environmental, microbial, and human antimicrobial resistance surveillance and risk analysis in the digital age.

Antimicrobial resistance (AMR) and antibiotic resistance (ABR) represent one of the most pressing global health threats, driven by the complex interpl...

Assessing the clinical deployability of AI tools for caries detection: A structured narrative synthesis and proposed framework.

OBJECTIVE: To synthesise how studies evaluating AI-based caries detection on bitewing radiographs report evidence relevant to real-world clinical use ...

A Dynamic Machine Learning Approach to Complement Nurse-Led Clinics in Identifying High-Risk Patients with Intermittent Claudication.

OBJECTIVE: To develop and validate dynamic machine-learning models to predict 6-month adverse outcomes in intermittent claudication (IC), enabling ris...

Evaluation and enhancement of the prognostic ability of the eighth edition of TNM staging in cutaneous malignant melanoma: a population-based study, using machine learning, of 111 871 patients.

BACKGROUND: Melanoma is the fifth most common cancer in the USA and the UK, with global incidence on the rise. The TNM staging system guides treatment...

Augmenting prion surveillance by immunohistochemistry using artificial intelligence-based image analysis.

Chronic wasting disease (CWD) and scrapie are transmissible spongiform encephalopathy diseases caused by prions, infectious forms of the prion protein...

Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study.

BACKGROUND: Artificial intelligence (AI)-based conversational tools are rapidly expanding within mental health care as a means of increasing access an...

A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning.

Respiratory disease outbreaks burden American healthcare systems with over one million hospitalizations annually, yet current surveillance systems lag...

A multi-class framework for face mask compliance detection using lightweight deep learning models.

The use of face mask compliance regarding compliance monitoring is important in health sensitive and surveillance driven environments. This paper has ...

From data to decision: a clinical pipeline for wearable AI in early cardiotoxicity detection.

Chemotherapy-induced cardiotoxicity (CIC) is a leading cause of morbidity in cancer survivors, as conventional surveillance often detects cardiac dysf...

The evolving PI-RADS paradigm.

Prostate MRI is central to the diagnostic pathway for prostate cancer (PCa), reducing unnecessary biopsies, improving the detection of clinically sign...

Global whole-genome, phylodynamic, and machine-learning analysis of Glaesserella parasuis serovars 2, 5, and 12.

Glaesserella parasuis is a respiratory pathogen of swine and the causative agent of Glässer's disease. Among the 15 serotypes, serotypes 2, 5, and 12 ...

Consensus statement on the application of artificial intelligence in osteoporosis screening and management: perspectives from the Asia-Pacific region.

UNLABELLED: Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due ...

Human rights violations reporting dataset.

Human rights research increasingly employs computational text analysis, but existing datasets provide either document-level aggregations or event-leve...

Accelerated biological aging in bipolar disorder as determined by artificial intelligence-based electrocardiographic assessment.

BACKGROUND: Individuals with bipolar disorder (BD) are at increased risk for major adverse cardiovascular events. Recent evidence suggests that the di...

The role of whole genome sequencing in antimicrobial susceptibility prediction of bacteria: 2025 update from the EUCAST Subcommittee.

SCOPE: The 2017 European Committee on Antimicrobial Susceptibility Testing (EUCAST) subcommittee report on the role of Whole Genome Sequencing (WGS) i...

Unveiling the gap in heart failure: a Brazilian Unified Health System study.

BACKGROUND: Heart failure (HF) decompensation is the leading cause of hospitalisations in developed countries and the third most common cause in Brazi...

Evaluating Crowdsourced Data Collection for Carceral Death Surveillance: Pilot Study Using Amazon Mechanical Turk.

BACKGROUND: People who are incarcerated face significantly higher health risks than the general population, yet deaths in custody remain underreported...