AI pitfalls and what not to do: mitigating bias in AI
Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …
healthcare systems. As the use of these applications increases, we are learning the failures …
Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements
Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic
decision‐making (DM), these systems have found wide‐ranging applications across diverse …
decision‐making (DM), these systems have found wide‐ranging applications across diverse …
On the analyses of medical images using traditional machine learning techniques and convolutional neural networks
Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model …
Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
Quantifying uncertainty in deep learning of radiologic images
In recent years, deep learning (DL) has shown impressive performance in radiologic image
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …
Understanding and mitigating bias in imaging artificial intelligence
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model
development, with potential for exacerbating health disparities. However, bias in imaging AI …
development, with potential for exacerbating health disparities. However, bias in imaging AI …
Value creation through artificial intelligence and cardiovascular imaging: a scientific statement from the American Heart Association
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular
imaging are being proposed and developed. However, the processes involved in …
imaging are being proposed and developed. However, the processes involved in …
Clinical, cultural, computational, and regulatory considerations to deploy AI in radiology: perspectives of RSNA and MICCAI experts
The Radiological Society of North of America (RSNA) and the Medical Image Computing
and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and …
and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and …
Radiomics applications in head and neck tumor imaging: a narrative review
M Tortora, L Gemini, A Scaravilli, L Ugga… - Cancers, 2023 - mdpi.com
Simple Summary Head and neck tumors (HNTs) are associated with a high mortality due to
their commonly insidious and asymptomatic development. Regarding risk stratification and …
their commonly insidious and asymptomatic development. Regarding risk stratification and …
[HTML][HTML] Image-based generative artificial intelligence in radiology: comprehensive updates
Generative artificial intelligence (AI) has been applied to images for image quality
enhancement, domain transfer, and augmentation of training data for AI modeling in various …
enhancement, domain transfer, and augmentation of training data for AI modeling in various …