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Clinical Trials as Topic

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Artificial intelligence and blockchain in clinical trials: enhancing data governance efficiency, integrity, and transparency.

Bioanalysis
This article examines the transformative potential of blockchain technology and its integration with artificial intelligence (AI) in clinical trials, focusing on their combined ability to enhance integrity, operational efficiency, and transparency in...

Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review.

Artificial intelligence in medicine
INTRODUCTION: There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The goal of this systematic review is to assess the impact of implementing AI approaches on different asp...

The use of Artificial Intelligence Algorithms in drug development and clinical trials: A scoping review.

International journal of medical informatics
BACKGROUND: Artificial Intelligence (AI) is transforming drug development and clinical trials, helping researchers find new treatments faster and personalize care for patients. By automating tasks like molecule screening and predicting treatment outc...

Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.

The journal of prevention of Alzheimer's disease
BACKGROUND: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by exper...

ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.

JCO clinical cancer informatics
PURPOSE: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based ...

Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods.

Therapeutic innovation & regulatory science
The traditional clinical trial monitoring process, which relies heavily on site visits and manual review of accumulative patient data reported through Electronic Data Capture system, is time-consuming and resource-intensive. The recently emerged risk...

Diffused responsibilities in technology-driven health research: The case of artificial intelligence systems in decentralized clinical trials.

Drug discovery today
Innovations such as artificial intelligence (AI) and decentralized clinical trials (DCTs) offer opportunities to enhance trial quality and efficiency. However, these innovations raise ethical questions about key responsibilities in research, such as ...

The use of large language models to enhance cancer clinical trial educational materials.

JNCI cancer spectrum
BACKGROUND: Adequate patient awareness and understanding of cancer clinical trials is essential for trial recruitment, informed decision making, and protocol adherence. Although large language models (LLMs) have shown promise for patient education, t...

Semi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of P...

Analysis of eligibility criteria clusters based on large language models for clinical trial design.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Clinical trials (CTs) are essential for improving patient care by evaluating new treatments' safety and efficacy. A key component in CT protocols is the study population defined by the eligibility criteria. This study aims to evaluate the...