Bioterrorism

Navigating the Artificial Intelligence Integration Challenge: One Nursing School's Journey From Uncertainty to Systematic Action.

BACKGROUND: With the rapid advancement of artificial intelligence (AI) across education and health care, nursing programs are increasingly leveraging AI to strengthen faculty and students' professional knowledge, skills, and preparedness for future practice. PROBLEM: Utilization and integration of AI into schools of nursing lacks clear, pedagogically sound guidance and systematic discipline-specif...

Cross-cultural adaptation and psychometric evaluation of the Shinners Artificial Intelligence Perception Scale (SAIP): evidence from Turkish nursing students.

BACKGROUND: Integrating artificial intelligence (AI) technologies into nursing practice is expanding rapidly, yet validated instruments to assess nursing students' perceptions of AI remain scarce in non-English-speaking contexts. The Shinners Artificial Intelligence Perception (SAIP) Scale was developed to measure healthcare professionals' perceptions of AI; however, its applicability in Turkish c...

Machine Learning Models for Prediction of Response to Therapeutic Plasma Exchange in Pediatric Acute Liver Failure.

BACKGROUND: Therapeutic plasma exchange (TPE) artificially lowers prognostic markers such as INR and bilirubin, complicating timely decisions regardin...

DeepSynBa: Actionable Drug Combination Prediction with Complete Dose-Response Profiles.

Many cancer monotherapies demonstrate limited clinical efficacy, making combination therapies a relevant treatment strategy. The extensive number of p...

Artificial intelligence chain-of-thought reasoning in nuanced medical scenarios: mitigation of cognitive biases through model intransigence.

BACKGROUND: Artificial intelligence large language models (LLMs) are increasingly used to inform clinical decisions but sometimes exhibit human-like c...

The Open Syndrome Definition as a Machine-Readable Standard for Public Health: Design and Implementation Study.

BACKGROUND: Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readab...

Psychometric performance and student perceptions of AI- versus student-generated multiple-choice questions: a single-center randomized controlled trial.

BACKGROUND: Developing high-quality multiple-choice examinations in medical education is time- and resource-intensive. Large language models (LLMs) of...

Development and validation of artificial intelligence-assisted volumetric response criteria in pleural mesothelioma (ARTIMES): a retrospective, multicohort, multicentre study.

BACKGROUND: Response evaluation in pleural mesothelioma is challenging because its crescent growth pattern is poorly captured by diameter-based criter...

Post-Pandemic Forecasting of Pediatric Acute Respiratory Infections with Deep Learning: A Multi-Pathogen, Multi-Time-Horizon Study.

BACKGROUND: Acute respiratory infections (ARIs) remain a major cause of morbidity and hospitalization in children worldwide. In the post-COVID-19 era,...

Combining machine learning and iterative experiments to keep pace with emerging viral variants of concern.

Modeling and predicting viral mutations before they emerge plays a crucial role in pandemic preparedness, enabling the early identification of emergin...

Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.

OBJECTIVES: To develop a nomogram model for individualized prediction of neoadjuvant chemotherapy (NAC) response in locally advanced laryngeal cancer ...

Proteomic and machine learning analysis predicts treatment response signatures in Myasthenia Gravis.

BACKGROUND: Myasthenia gravis (MG) is a prototypical antibody-mediated autoimmune disease with variable treatment responses with a need for biomarkers...

Experts' Opinions on the Sustainable Use of Digital Health Tools for Effective Future Pandemic Preparedness and Response: Questionnaire Study.

BACKGROUND: The COVID-19 pandemic demonstrated the potential role of digital health tools in enhancing pandemic preparedness and response. These tools...

Development and evaluation of a large language model-based, retrieval-augmented generation application for query response in early oncology clinical trials.

BACKGROUND: Early-phase oncology trials involve complex protocols and extensive documents, making timely resolution of study queries challenging. We d...

Identifying hepatocellular carcinoma patients at risk of early non-response after first-cycle transarterial chemoembolization: A reproducible machine learning study.

PURPOSE: Transarterial chemoembolization (TACE) is standard therapy for intermediate-stage hepatocellular carcinoma (HCC), but early non-response afte...

Phenotypic response surfaces in pharmacology: Mechanistic and translational perspectives on optimizing drug combination therapies.

Combination drug therapies are central to the treatment of diseases with multifactorial etiology, including cancer, infectious diseases, and autoimmun...

Deep learning completes US flood hazard maps revealing millions exposed to previously unrecognized risk.

Flood maps are essential for disaster preparedness, insurance and climate adaptation, yet US official maps cover only one-third of river channels. Her...

Development and validation of a radiomics-dosiomics model for predicting radiotherapy response in locally advanced, unresectable non-small cell lung cancer: a multi-center study.

Early assessment of treatment response is essential for optimizing cancer management, as it allows timely interventions during the course of therapy, ...

Longitudinal validation of ENLIGHT, an AI predictor of immunotherapy response and resistance, in pan-cancer cohorts.

Artificial intelligence (AI) algorithms such as ENLIGHT and DeepPT represent promising approaches to identify predictive biomarkers for immune checkpo...

Robust automated detection of small-scale rainfall-induced landslides in Italy Using SegFormer and high-resolution satellite imagery.

Rainfall-induced landslides pose significant challenges in Italy due to their small scale, fragmented distribution, and the severe damage they cause t...