AI Medical Compendium Journal:
Current problems in diagnostic radiology

Showing 1 to 10 of 23 articles

Quality improvement project: Patient-centered breast imaging letters.

Current problems in diagnostic radiology
PURPOSE: Assess patient-centered revisions to our institution's screening mammography letters for BIRADS-0 and BIRADS-0 dense breast employing existing validated readability and usability rating instruments.

Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.

Current problems in diagnostic radiology
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...

Release of complex imaging reports to patients, do radiologists trust AI to help?

Current problems in diagnostic radiology
BACKGROUND: As a result of the 21st Century Cures Act, radiology reports are immediately released to patients. However, these reports are often too complex for the lay patient, potentially leading to stress and anxiety. While solutions such as patien...

Exploring the integration of artificial intelligence in radiology education: A scoping review.

Current problems in diagnostic radiology
BACKGROUND: The integration of Artificial Intelligence (AI) into radiology education presents a transformative opportunity to enhance learning and practice within the field. This scoping review aims to systematically explore and map the current lands...

Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system.

Current problems in diagnostic radiology
PURPOSE: To validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intelligence Thyroid Imaging Reporting and Data System (AI TI-RADS).

ChatGPT and assistive AI in structured radiology reporting: A systematic review.

Current problems in diagnostic radiology
INTRODUCTION: The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential in structured radiology reportin...

Part I: prostate cancer detection, artificial intelligence for prostate cancer and how we measure diagnostic performance: a comprehensive review.

Current problems in diagnostic radiology
MRI has firmly established itself as a mainstay for the detection, staging and surveillance of prostate cancer. Despite its success, prostate MRI continues to suffer from poor inter-reader variability and a low positive predictive value. The recent e...

Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.

Current problems in diagnostic radiology
Breast cancer is the most common type of cancer in women, and early abnormality detection using mammography can significantly improve breast cancer survival rates. Diverse datasets are required to improve the training and validation of deep learning ...

Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT and Bard.

Current problems in diagnostic radiology
Artificial intelligence (AI) has recently become a trending tool and topic regarding productivity especially with publicly available free services such as ChatGPT and Bard. In this report, we investigate if two widely available chatbots chatGPT and B...

Analysis of ChatGPT publications in radiology: Literature so far.

Current problems in diagnostic radiology
OBJECTIVE: To perform a detailed qualitative and quantitative analysis of the published literature on ChatGPT and radiology in the nine months since its public release, detailing the scope of the work in the short timeframe.