[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
Human-AI symbiosis: a path forward to improve chest radiography and the role of radiologists in patient care
To start, we need more rigorous testing of algorithms with prospective, pragmatic, real-world
clinical trials in diverse settings to assure robust generalizability, lack of biases, and a high …
clinical trials in diverse settings to assure robust generalizability, lack of biases, and a high …
[HTML][HTML] Uncover this tech term: uncertainty quantification for deep learning
Uncover This Tech Term: Uncertainty Quantification for Deep Learning - PMC Back to Top
Skip to main content NIH NLM Logo Access keys NCBI Homepage MyNCBI Homepage …
Skip to main content NIH NLM Logo Access keys NCBI Homepage MyNCBI Homepage …
Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI
SJ Fransen, TC Kwee, D Rouw, C Roest… - European …, 2024 - Springer
Objectives This study investigated patients' acceptance of artificial intelligence (AI) for
diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI …
diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI …
Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection
D Peeters, KV Venkadesh, R Dinnessen… - Computers in Biology …, 2025 - Elsevier
Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied
to unseen data that differs from the training data distribution, referred to as dataset shift …
to unseen data that differs from the training data distribution, referred to as dataset shift …
Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation
D Peeters, N Alves, KV Venkadesh, R Dinnessen… - European …, 2024 - Springer
Objective To investigate the effect of uncertainty estimation on the performance of a Deep
Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. Methods and …
Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. Methods and …
Can Uncertainty Quantification of AI Prediction Models Benefit Young Radiologists More?
X Bai, Z Jin, H Sun - Radiology, 2024 - pubs.rsna.org
Editor: We read with great interest the study by Dr Alves and colleagues (1), published in the
September 2023 issue of Radiology, about uncertainty quantification (UQ) of artificial …
September 2023 issue of Radiology, about uncertainty quantification (UQ) of artificial …
AI Predictive Uncertainty: A Step Forward
PS Babyn - Radiology, 2023 - pubs.rsna.org
Dr Paul Babyn is a pediatric radiologist with research interest in applications of artificial
intelligence to medical imaging, especially pediatric imaging and human-computer …
intelligence to medical imaging, especially pediatric imaging and human-computer …
A framework for assessing joint human-AI systems based on uncertainty estimation
We investigate the role of uncertainty quantification in aiding medical decision-making.
Existing evaluation metrics fail to capture the practical utility of joint human-AI decision …
Existing evaluation metrics fail to capture the practical utility of joint human-AI decision …
From out-of-distribution detection to quality control
Quality Control (QC) is an important step of any medical image analysis pipeline to impose
safeguards against biased interpretation. Visual QC can be tedious and time-consuming …
safeguards against biased interpretation. Visual QC can be tedious and time-consuming …