AIMC Topic: United States Food and Drug Administration

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Comparative safety profiling of sodium zirconate cyclosilicate and patiromer using real-world FAERS data: A pharmacovigilance analysis.

Medicine
This study aimed to detect and contrast the adverse drug event (ADE) signals associated with sodium zirconate cyclosilicate (SZC) and Patiromer by leveraging the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), thereby in...

Artificial Intelligence in Dermatology: A Comprehensive Review of Approved Applications, Clinical Implementation, and Future Directions.

International journal of dermatology
This comprehensive review examines artificial intelligence (AI) applications in dermatology, approved by the United States (U.S.) Food and Drug Administration (FDA) and international organizations, evaluating their clinical implementation and impact ...

Role of AI in Clinical Decision-Making: An Analysis of FDA Medical Device Approvals.

Studies in health technology and informatics
The U.S. Food and Drug Administration (FDA) plays an important role in ensuring safety and effectiveness of AI/ML-enabled devices through its regulatory processes. In recent years, there has been an increase in the number of these devices cleared by ...

Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Molecular diversity
Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; ...

Navigating FDA Regulations for the Development of Artificial Intelligence Technologies in Plastic Surgery.

Aesthetic surgery journal
Artificial intelligence (AI) technologies are rapidly transforming the field of plastic surgery, offering new opportunities for improving patient outcomes through enhanced diagnostic capabilities, personalized treatment planning, and outcome predicti...

BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Chemical research in toxicology
Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early prediction of DILI is crucial for developing safer pharmaceuticals, yet current and testing methods are...

Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches.

Journal of biomolecular structure & dynamics
Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifical...

FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine.

JAMA
IMPORTANCE: Advances in artificial intelligence (AI) must be matched by efforts to better understand and evaluate how AI performs across health care and biomedicine as well as develop appropriate regulatory frameworks. This Special Communication revi...

A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration.

The western journal of emergency medicine
INTRODUCTION: Medical device recalls are important to the practice of emergency medicine, as unsafe devices include many ubiquitous items in emergency care, such as vascular access devices, ventilators, infusion pumps, video laryngoscopes, pulse oxim...

Implications of An Evolving Regulatory Landscape on the Development of AI and ML in Medicine.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The rapid advancement of artificial intelligence and machine learning (AI/ML) technologies in healthcare presents significant opportunities for enhancing patient care through innovative diagnostic tools, monitoring systems, and personalized treatment...