AIMC Topic: Patient Selection
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AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases.
Nature medicine
Aug 7, 2024
Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impac...
AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial.
Nature medicine
Jul 8, 2024
Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the numbe...
Use of natural language processing techniques to predict patient selection for total hip and knee arthroplasty from radiology reports.
The bone & joint journal
Jul 1, 2024
AIMS: To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology ...
Analysis of factors that indicated surgery in 400 patients submitted to a complete diagnostic workup for obstructed defecation syndrome and rectal prolapse using a supervised machine learning algorithm.
Techniques in coloproctology
Jun 25, 2024
BACKGROUND: Patient selection is extremely important in obstructed defecation syndrome (ODS) and rectal prolapse (RP) surgery. This study assessed factors that guided the indications for ODS and RP surgery and their specific role in our decision-maki...
Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.
Nature communications
Jun 1, 2024
Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful de...
Artificial intelligence and nonoperating room anesthesia.
Current opinion in anaesthesiology
May 16, 2024
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient sel...
A multiview deep learning-based prediction pipeline augmented with confident learning can improve performance in determining knee arthroplasty candidates.
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
May 7, 2024
PURPOSE: Preoperative prudent patient selection plays a crucial role in knee osteoarthritis management but faces challenges in appropriate referrals such as total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA) and nonoperative inte...
[Development of an artificial intelligence system to improve cancer clinical trial eligibility screening].
Bulletin du cancer
Mar 19, 2024
INTRODUCTION: The recruitment step of all clinical trials is time consuming, harsh and generate extra costs. Artificial intelligence tools could improve recruitment in order to shorten inclusion phase. The objective was to assess the performance of a...
Artificial intelligence tools for optimising recruitment and retention in clinical trials: a scoping review protocol.
BMJ open
Mar 19, 2024
INTRODUCTION: In recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought about significant improvements in the efficiency and cost reduction of clinical trials. The objective o...
Enhancing site selection strategies in clinical trial recruitment using real-world data modeling.
PloS one
Mar 11, 2024
Slow patient enrollment or failing to enroll the required number of patients is a disruptor of clinical trial timelines. To meet the planned trial recruitment, site selection strategies are used during clinical trial planning to identify research sit...
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