Surveillance

Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review.

BACKGROUND: Intraoperative bleeding is a critical event that impacts surgical safety and patient outcomes. Machine learning (ML) has demonstrated potential in prediction tasks, yet its methodological rigor and clinical translation face challenges. OBJECTIVE: This scoping review aims to systematically synthesize the current state of development, performance, and validation of ML models for predicti...

Informatics-Based Psychotherapeutic and Psychiatric Interventions in Dermatology: Scoping Review of Impacts on Skin Disease Severity and Mental Health Outcomes.

BACKGROUND: Chronic dermatologic conditions such as psoriasis, atopic dermatitis, and hidradenitis suppurativa are associated with a high burden of psychiatric comorbidities, including depression, anxiety, and suicidality. Despite growing awareness of the psychosocial impact of skin diseases, mental health needs remain underaddressed in dermatologic care. Digital technologies (including teledermat...

Evaluating a Multitask AI Model versus Humans for Portion Size Estimation.

BACKGROUND: Accurate dietary assessment is essential for precision nutrition and nutrition surveillance. Portion size estimation remains challenging, ...

Prospective validation of an AI algorithm to identify adult-onset type 1 diabetes misclassification: protocol for a non-interventional multicentre study.

INTRODUCTION: Adult-onset type 1 diabetes (T1D) is often misclassified as type 2 diabetes (T2D), resulting in delayed treatment, missed opportunities ...

AI-assisted extraction of opportunistic findings from oncologic CT reports using large language models.

Opportunistic findings at imaging (iOFs), such as osteoporosis, liver steatosis, or coronary artery calcifications, are clinically relevant abnormalit...

Benchmarking deep learning architectures for MALDI-TOF mass spectrometry in infectious disease diagnostics and vector-borne disease surveillance.

Emerging global health threats, from antimicrobial resistance to vector-borne diseases, require scalable diagnostic solutions. Matrix-assisted laser d...

A Retrieval-Augmented Natural Language Interface for Data Description and Meta-Analysis in the Pathogens-in-Foods (PIF) Database.

Food-safety occurrence databases are increasingly important for surveillance, evidence appraisal, and quantitative risk assessment, yet their routine ...

Predicting Harm: A Systematic Review of Pediatric Pressure Injury Models and Their Clinical Shortcomings.

SIGNIFICANCE: Pediatric pressure injuries (PIs) are a distinct and preventable clinical challenge, yet risk prediction models tailored to children rem...

Identifying communities at elevated risk of hepatitis C infection in Los Angeles County: an ecological study using machine learning.

BACKGROUND: Identifying communities disproportionately affected by hepatitis C infection is essential for targeted prevention and resource allocation....

AquaAI: development and internal validation of a Danish transformer-based model to identify drowning and aquatic incidents in prehospital medical records.

BACKGROUND: Effective prevention of drowning and aquatic incidents requires timely and accurate surveillance supported by high-quality validated data....

Advancing One Health genomics in Africa: opportunities and challenges for outbreak and antimicrobial resistance control.

SUMMARYAfrica's ongoing struggles with emerging epidemics and antimicrobial resistance (AMR) underscore the urgency of integrating pathogen genomics a...

Integrating google trends and hybrid statistical-machine learning models for dengue surveillance in an inland vietnamese province: a 94-month evaluation (2013-2021) with search-signal anomaly screening.

Dengue transmission in inland Southeast Asia shows strong seasonality and short-term surges that challenge timely public-health response. We assessed ...

Review Topic: Don't Cancer Doctors Offer Good-Quality Care?

We offer the perspectives of two veterans on the last quarter century of quality improvement efforts in oncology care. We believe that our colleagues ...

Digital Hydrogel Fluorescent-Enhancing Microspheres via CRISPR/Cas12a for Amplification-Free Relative Quantification of Nucleic Acids.

CRISPR/Cas systems hold great promise for molecular diagnostics, but their amplification-free applications are hampered by weak signals and poor quant...

Reinforcement learning for policymaking in epidemic control: A scoping review.

BACKGROUND: Managing an epidemic demands policies that respond at the pace of the outbreak. Conventional rule‑based interventions struggle to keep up,...

Human-in-the-Loop Governance of Artificial Intelligence in Cardiology: From Ethical Principles to Operational Paradigms.

BACKGROUND: Artificial intelligence (AI) is transforming cardiology across ECG interpretation, imaging, risk prediction, remote monitoring, and workfl...

G.AI: an AI-driven platform for phenotype standardization, variant interpretation and structured clinical reporting in rare disease genomic diagnosis.

BACKGROUND: The diagnosis of rare diseases increasingly relies on the interpretation of high-throughput next-generation sequencing (NGS) data. As sequ...

Microplastics across marine food webs: bioaccumulation, exposome interactions, and emerging oncogenic risks.

Microplastic pollution is a major environmental problem affecting ecosystems, wildlife and people worldwide. Microplastics are solid synthetic polymer...

Open Diagnostic Reader (ODR): An affordable, modular 3D-printed platform for standardized imaging and quantitative analysis of rapid diagnostic tests.

Rapid diagnostic tests (RDTs) are key to disease surveillance, yet their interpretation remains challenging due to weak test lines and difficulties in...

Grading and Reporting of Graft-versus-Host Disease in the Modern Era.

Graft-versus-host disease (GVHD) continues to be a major cause of morbidity after allogeneic stem cell transplantation. This article summarizes curren...