Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
Algorithm fairness in ai for medicine and healthcare
In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …
Review of Disentanglement Approaches for Medical Applications--Towards Solving the Gordian Knot of Generative Models in Healthcare
J Fragemann, L Ardizzone, J Egger… - arXiv preprint arXiv …, 2022 - arxiv.org
Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …
segmentation, or classification. Besides this, they are often criticized as black boxes as their …
Location-Aware Encoding for Lesion Detection in Ga-DOTATATE Positron Emission Tomography Images
Objective: Lesion detection with positron emission tomography (PET) imaging is critical for
tumor staging, treatment planning, and advancing novel therapies to improve patient …
tumor staging, treatment planning, and advancing novel therapies to improve patient …
BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping
Recent advances in machine learning and deep learning have presented new opportunities
for learning to localize the origin of ventricular activation from 12-lead electrocardiograms …
for learning to localize the origin of ventricular activation from 12-lead electrocardiograms …
Deep learning for personalized electrocardiogram diagnosis: A review
The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its
interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep …
interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep …
[HTML][HTML] Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder
M Kapsecker, MC Möller, SM Jonas - Computers in Biology and Medicine, 2025 - Elsevier
Wearable technology enables the unsupervised recording of electrocardiogram (ECG)
signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical …
signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical …
Non-invasive localization of the ventricular excitation origin without patient-specific geometries using deep learning
Cardiovascular diseases account for 17 million deaths per year worldwide. Of these, 25%
are categorized as sudden cardiac death, which can be related to ventricular tachycardia …
are categorized as sudden cardiac death, which can be related to ventricular tachycardia …