Equipment and Supplies Commons^™

What Are The Current Risks For Patients Associated With Technology Related Perfusion Practices: A Scoping Review, Jessica Steel

Student Works

Background: Advancements in medical technology, particularly in perfusion practices such as ECMO and heart-lung bypass, have improved patient care but also introduced novel risks. Technological errors—ranging from hardware malfunctions to software failures and AI model inaccuracies—can directly threaten patient safety.

Objective: To identify current trends and risks associated with technology in perfusion practices and synthesize evidence on the frequency, nature, and outcomes of technology-related errors.

Results: Evidence indicates substantial deficiencies in medical devices, predictive models, and health IT systems. AI-based predictive models frequently failed to detect critical health deterioration, missing 66% of injuries in some in-hospital mortality simulations. Adverse events …

Unveiling The Potential: The Role Of Ai-Enhanced Ecg In Cardiovascular Disease Detection, Alisha Vincent

Rowan-Virtua Research Day

Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and …

Patient And Provider Experience With Artificial Intelligence Screening Technology For Diabetic Retinopathy In A Rural Primary Care Setting, Brian M. Nolan, Emma R. Daybranch, Kerri Barton, Neil Korsen

Journal of Maine Medical Center

Introduction: The development of autonomous artificial intelligence for interpreting diabetic retinopathy (DR) images has allowed for point-of-care testing in the primary care setting. This study describes patient and provider experiences and perceptions of the artificial intelligence DR screening technology called EyeArt by EyeNuk during implementation of the tool at Western Maine Primary Care in Norway, Maine.

Methods: This non-randomized, single-center, prospective observational study surveyed 102 patients and 13 primary care providers on their experience of the new screening intervention.

Results: All surveyed providers agreed that the new screening tool would improve access and annual screening rates. Some providers also identified …

Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virigina B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai

Electrical & Computer Engineering Faculty Research

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …