AIMC Topic: Patient Selection
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Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.
Science advances
Jun 4, 2025
Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factor...
Accuracy of machine learning in identifying candidates for total knee arthroplasty (TKA) surgery: a systematic review and meta-analysis.
European journal of medical research
Apr 22, 2025
BACKGROUND: The application of machine learning (ML) in predicting the requirement for total knee arthroplasty (TKA) at knee osteoarthritis (KOA) patients has been acknowledged. Nonetheless, the variables employed in the development of ML models are ...
Large language models for automating clinical trial matching.
Current opinion in urology
Mar 20, 2025
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
Mar 18, 2025
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...
Managing class imbalance in the training of a large language model to predict patient selection for total knee arthroplasty: Results from the Artificial intelligence to Revolutionise the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project.
The Knee
Feb 28, 2025
INTRODUCTION: This study set out to test the efficacy of different techniques used to manage to class imbalance, a type of data bias, in application of a large language model (LLM) to predict patient selection for total knee arthroplasty (TKA).
Epilepsy surgery candidate identification with artificial intelligence: An implementation study.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Feb 22, 2025
BACKGROUND: To (a) evaluate the effect of a machine learning algorithm in the identification of patients suitable for epilepsy surgery evaluation, and (b) examine the performance of a large language model (LLM) in the collation of key pieces of infor...
Redefining prostate cancer care: innovations and future directions in active surveillance.
Current opinion in urology
Feb 17, 2025
PURPOSE OF REVIEW: This review provides a critical analysis of recent advancements in active surveillance (AS), emphasizing updates from major international guidelines and their implications for clinical practice.
Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.
Journal of medical Internet research
Jan 14, 2025
BACKGROUND: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment methodologies is crucial for the success of such trials. Evidence on whether web-based recruitment meth...
ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.
JCO clinical cancer informatics
Jan 9, 2025
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 ...
Artificial Intelligence-Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases.
Journal of vascular and interventional radiology : JVIR
Dec 3, 2024
PURPOSE: To develop a machine learning algorithm to improve hepatic resection selection for patients with metastatic colorectal cancer (CRC) by predicting post-portal vein embolization (PVE) outcomes.
Aged
Embolization, Therapeutic
Retrospective Studies
Male
Predictive Value of Tests
Portal Vein
Humans
Clinical Decision-Making
Middle Aged
Patient Selection
Patient-Specific Modeling
Liver Neoplasms
Adult
Machine Learning
Hepatectomy
Female
Liver Regeneration
Treatment Outcome
Reproducibility of Results
Colorectal Neoplasms
Decision Support Techniques
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