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Pharmacovigilance in Cell and Gene Therapy: Evolving Challenges in Risk Management and Long-Term Follow-Up.

Cell and gene therapies, including CAR T-cells, CRISPR-based genome editing, and next-generation vir...

Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models.

Accurate forecasting of diabetes burden is essential for healthcare planning, resource allocation, a...

Artificial intelligence in pharmacovigilance signal management: a review of tools, implementations, research, and regulatory landscape.

INTRODUCTION: The integration of artificial intelligence (AI) into pharmacovigilance (PV) has advanc...

Machine learning-assisted design of cathode materials for lithium-sulfur batteries derived from a metal-organic framework.

Designing cathode materials is crucial for developing advanced Li-S batteries, but conventional tria...

Improvements from incorporating machine learning algorithms into near real-time operational post-processing.

During regional seismic monitoring, data is automatically analyzed in real-time to identify events a...

Pharmacovigilance: Overview of Italian and European regulations, tools, and perspectives.

BackgroundThis study provides a concise overview of the Italian and European pharmacovigilance (PV) ...

Predicting post-traumatic stress disorder in relatives of critically ill patients.

PURPOSE OF REVIEW: Symptoms of posttraumatic stress disorder (PTSD) affect up to a third of relative...

Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial.

OBJECTIVES: This study aimed to evaluate the potential additional value of deep radiomics for assess...

Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.

Current evidence for predictive models of post-stroke depression (PSD) risk based on machine learnin...

In Silico Digital Breast Tomosynthesis Dataset for the Comparative Analysis of Deep Learning Models in Tumor Segmentation.

The scarcity of publicly available digital breast tomosynthesis (DBT) datasets significantly limits ...

NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search.

Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expen...

Performance of Clinical Risk Prediction Models for Post-ERCP Pancreatitis: A Systematic Review.

OBJECTIVES: Pancreatitis is common following endoscopic retrograde cholangiopancreatography (ERCP). ...

β-lactam resistance: epidemiological trends, molecular drivers, and innovative control strategies in the post-pandemic era.

SUMMARY () is a major human pathogen that can cause severe diseases such as meningitis and bacteremi...

The need for guardrails with large language models in pharmacovigilance and other medical safety critical settings.

Large language models (LLMs) are useful tools with the capacity for performing specific types of kno...

Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.

BACKGROUND: Pharmacovigilance is vital for monitoring adverse drug reactions (ADRs) and ensuring dru...

Evaluating the impact of an AI-powered chatbot on epilepsy education and stigma reduction: A pre-post intervention study using EpiloBot.

OBJECTIVE: Effective epilepsy management requires accurate epilepsy knowledge, active patient engage...

Quantification of hepatic steatosis on post-contrast computed tomography scans using artificial intelligence tools.

PURPOSE: Early detection of steatotic liver disease (SLD) is critically important. In clinical pract...

Predicting outcomes following endovascular aortoiliac revascularization using machine learning.

Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease...

Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.

BACKGROUND: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classificati...

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