AI Medical Compendium Topic

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Product Surveillance, Postmarketing

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Leveraging Contextual Information in Extracting Long Distance Relations from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Relation extraction from biomedical text is important for clinical decision support applications. In post-marketing pharmacovigilance, for example, Adverse Drug Events (ADE) relate medical problems to the drugs that caused them and were the focus of ...

Machine Learning Approach for Active Vaccine Safety Monitoring.

Journal of Korean medical science
BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active...

Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies.

Sovremennye tekhnologii v meditsine
UNLABELLED: was to develop a methodology for conducting post-registration clinical monitoring of software as a medical device based on artificial intelligence technologies (SaMD-AI).

Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.

Journal of medical Internet research
Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of it...

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

Drug safety
INTRODUCTION: The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse ev...

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

JAMA network open
IMPORTANCE: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA...

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Patient monitors case study.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundHealthcare institutions throughout the world rely on medical devices to provide their services reliably and effectively. However, medical devices can, and do sometimes fail. These failures pose significant risk to patients.ObjectiveOne way ...

Advancement of post-market surveillance of medical devices leveraging artificial intelligence: Infusion pumps case study.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundAnalysis of data from incident registries such as MAUDE has identified the need to improve surveillance and maintenance strategies for infusion pumps to enhance patient and healthcare staff safety.ObjectiveThe ultimate goal is to enhance in...

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control,...

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning cu...