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Clinical Trials as Topic

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Machine learning prediction and explanatory models of serious infections in patients with rheumatoid arthritis treated with tofacitinib.

Arthritis research & therapy
BACKGROUND: Patients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learn...

Zero- and few-shot prompting of generative large language models provides weak assessment of risk of bias in clinical trials.

Research synthesis methods
Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of availabl...

CLARA-MeD Tool - A System to Help Patients Understand Clinical Trial Announcements and Consent Forms in Spanish.

Studies in health technology and informatics
We present an NLP web-based tool to help users understand consent forms (CFs) and clinical trial announcements (CTAs) in Spanish. For complex word identification, we collected: 1) a lexicon of technical terms and simplified synonyms (14 465 entries);...

Accelerating drug discovery, development, and clinical trials by artificial intelligence.

Med (New York, N.Y.)
Artificial intelligence (AI) has profoundly advanced the field of biomedical research, which also demonstrates transformative capacity for innovation in drug development. This paper aims to deliver a comprehensive analysis of the progress in AI-assis...

Machine learning and natural language processing in clinical trial eligibility criteria parsing: a scoping review.

Drug discovery today
Automatic eligibility criteria parsing in clinical trials is crucial for cohort recruitment leading to data validity and trial completion. Recent years have witnessed an explosion of powerful machine learning (ML) and natural language processing (NLP...

Artificial Intelligence and cancer: Profile of registered clinical trials.

Journal of cancer policy
Artificial Intelligence (AI) has made significant strides due to advancements in processing algorithms and data availability. Recent years have shown a resurgence in AI, driven by breakthroughs in deep machine learning. AI has attracted particular in...

Ethical Considerations in the Design and Conduct of Clinical Trials of Artificial Intelligence.

JAMA network open
IMPORTANCE: Safe integration of artificial intelligence (AI) into clinical settings often requires randomized clinical trials (RCT) to compare AI efficacy with conventional care. Diabetic retinopathy (DR) screening is at the forefront of clinical AI ...

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how t...

Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinica...

Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.

Annual review of pharmacology and toxicology
In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 he...