AIMC Topic: Clinical Trials as Topic

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NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.

International journal of radiation oncology, biology, physics
PURPOSE: Recent advances in artificial intelligence (AI) have showcased the potential of automatic treatment planning for clinical trials involving radiation therapy. This paper offers an overview of the current landscape of AI-based treatment planni...

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.

Neurology
BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We...

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023).

Journal of medical Internet research
BACKGROUND: Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.

Large language models for automating clinical trial matching.

Current opinion in urology
PURPOSE OF REVIEW: The uses of generative artificial intelligence (GAI) technologies in medicine are expanding, with the use of large language models (LLMs) for matching patients to clinical trials of particular interest. This review provides an over...

Utilising Natural Language Processing to Identify Brain Tumor Patients for Clinical Trials: Development and Initial Evaluation.

World neurosurgery
BACKGROUND: Identifying patients eligible for clinical trials through eligibility screening is time and resource-intensive. Natural Language Processing (NLP) models may enhance clinical trial screening by extracting data from Electronic Health Record...

Enhancing clinical trial outcome prediction with artificial intelligence: a systematic review.

Drug discovery today
Clinical trials are pivotal in drug development yet fraught with uncertainties and resource-intensive demands. The application of AI models to forecast trial outcomes could mitigate failures and expedite the drug discovery process. This review discus...

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 ...

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...

Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.

International journal of radiation oncology, biology, physics
This white paper examines the potential of pioneering technologies and artificial intelligence-driven solutions in advancing clinical trials involving radiation therapy. As the field of radiation therapy evolves, the integration of cutting-edge appro...