AIMC Topic: Quality Assurance, Health Care

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A semi-automated quality assurance tool for cardiovascular magnetic resonance imaging: application to outlier detection, artificial intelligence evaluation and trainee feedback.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular magnetic resonance (CMR) offers state-of-the-art volume, function, fibrosis and oedema imaging. Quality assurance (QA) tasks, such as quantitative parameter reproducibility assessments, the evaluation of AI methods, and the...

Quality assessment of patient-facing urologic telesurgery content using validated tools.

Journal of robotic surgery
INTRODUCTION: With increasing accessibility to Artificial Intelligence (AI) chatbots, the precision and clarity of medical information provided require rigorous assessment. Urologic telesurgery represents a complex concept that patients will investig...

AI-Driven quality assurance in mammography: Enhancing quality control efficiency through automated phantom image evaluation in South Korea.

PloS one
PURPOSE: To develop and validate a deep learning-based model for automated evaluation of mammography phantom images, with the goal of improving inter-radiologist agreement and enhancing the efficiency of quality control within South Korea's national ...

[Artificial intelligence under scrutiny: requirements, quality criteria, and testing tools for medical applications].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
The use of artificial intelligence (AI) in medicine has great potential to improve quality and efficiency. At the same time, its use is associated with risks. In order to prevent harm, experts from research and politics are developing requirements, t...

Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance.

PloS one
In this study, we address the inherent challenges in radiotherapy (RT) plan quality assessment (QA). RT, a prevalent cancer treatment, utilizes high-energy beams to target tumors while sparing adjacent healthy tissues. Typically, an RT plan is refine...

Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.

Oncotarget
Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability in he...

Development of an external quality assurance (EQA) structure to evaluate the quality of genetic pathology reporting.

Clinica chimica acta; international journal of clinical chemistry
A standard for reporting genetic pathology results currently does not exist as a consensus. While effective reports are produced, there is lack of consistency on which details to present or to emphasise, and the ultimate report often reflects an indi...

The Road Map for ACR Practice Accreditation for Radiology Artificial Intelligence.

Journal of the American College of Radiology : JACR
As the use of artificial intelligence (AI) continues to grow in radiology, it has become clear that its real-world performance often differs from that demonstrated in premarket testing, underscoring the need for robust quality management (QM) program...

Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.

Medical physics
BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in suc...

Artificial Intelligence in Endoscopy Quality Measures.

Gastrointestinal endoscopy clinics of North America
Quality of gastrointestinal endoscopy is a major determinant of its effectiveness. Artificial intelligence (AI) has the potential to enhance quality monitoring and improve endoscopy outcomes. This article reviews the current literature on AI algorith...