AI Medical Compendium Journal:
Radiographics : a review publication of the Radiological Society of North America, Inc

Showing 1 to 10 of 29 articles

Deep Learning-based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations.

Radiographics : a review publication of the Radiological Society of North America, Inc
Optimizing the CT acquisition parameters to obtain diagnostic image quality at the lowest possible radiation dose is crucial in the radiosensitive pediatric population. The image quality of low-dose CT can be severely degraded by increased image nois...

Bag-of-Words Technique in Natural Language Processing: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documen...

Generative Adversarial Networks: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of ge...

One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.

Radiographics : a review publication of the Radiological Society of North America, Inc
Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or excee...

Machine Learning for Medical Imaging.

Radiographics : a review publication of the Radiological Society of North America, Inc
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine lear...

Optimizing Large Language Models in Radiology and Mitigating Pitfalls: Prompt Engineering and Fine-tuning.

Radiographics : a review publication of the Radiological Society of North America, Inc
Large language models (LLMs) such as generative pretrained transformers (GPTs) have had a major impact on society, and there is increasing interest in using these models for applications in medicine and radiology. This article presents techniques to ...

State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.

Radiographics : a review publication of the Radiological Society of North America, Inc
The implementation of deep neural networks has spurred the creation of deep learning reconstruction (DLR) CT algorithms. DLR CT techniques encompass a spectrum of deep learning-based methodologies that operate during the different steps of the image ...

Overcoming "Fear of AI" Bias: Insights from the Technology Acceptance Model.

Radiographics : a review publication of the Radiological Society of North America, Inc

Understanding and Mitigating Bias in Imaging Artificial Intelligence.

Radiographics : a review publication of the Radiological Society of North America, Inc
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 is a complex topic that encompasses multiple coexisting definitions. m...