ChatGPT as an effective tool for quality evaluation of radiomics research.

Objectives: This study aimed to evaluate the effectiveness of ChatGPT-4o in assessing the methodological quality of radiomics research using the radiomics quality score (RQS) compared to human experts.

Methods: Published in European Radiology, European Radiology Experimental, and Insights into Imaging between 2023 and 2024, open-access and peer-reviewed radiomics research articles with creative commons attribution license (CC-BY) were included in this study. Pre-prints from MedRxiv were also included to evaluate potential peer-review bias.

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.

Purpose: Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducing morbidity and mortality. Artificial intelligence (AI) diagnosis support tools, such as Chat Generative Pre-trained Transformer (ChatGPT), offer rapid diagnostic advantages.

MRI radiomics based machine learning model of the periaqueductal gray matter in migraine patients.

Background And Purpose:

A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts.

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs).

Materials And Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images.

ChatGPT-assisted deep learning model for thyroid nodule analysis: beyond artifical intelligence.

Aims: To develop a deep learning model, with the aid of ChatGPT, for thyroid nodules, utilizing ultrasound images. The cytopathology of the fine needle aspiration biopsy (FNAB) serves as the baseline.

Material And Methods: After securing IRB approval, a retrospective study was conducted, analyzing thyroid ultrasound images and FNAB results from 1,061 patients between January 2017 and January 2022.

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of photon-counting CT provides superior image quality, reduced radiation dose, and material decomposition capabilities, while deep learning algorithms excel in automating image analysis and improving diagnostic accuracy. The integration of these technologies can lead to enhanced material decomposition and classification, spectral image analysis, predictive modeling for individualized medicine, workflow optimization, and radiation dose management.

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources.

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations.

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans.

Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach.

Background And Objectives: Early detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %.

Improving radiology workflow using ChatGPT and artificial intelligence.

Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language.

A mimicker of inflammatory breast carcinoma: Lupus mastitis and evolving imaging features.

Rare case of lupus mastitis in a 58-year-old female with discoid lupus erythematosus presented with fever, left breast swelling, and painful palpable lesion. Accurate imaging and histopathologic evaluation allowed for appropriate management and regression of breast findings with hydroxychloroquine treatment, emphasizing the need to avoid unnecessary biopsies and surgeries.

Differentiation of pure cystic sellar lesions on magnetic resonance imaging.

Background And Purpose: Cystic pituitary adenomas and cystic craniopharyngiomas may mimic Rathke cleft cysts when there is no solid enhancing component on magnetic resonance imaging (MRI). This study aims to investigate the efficiency of MRI findings in differentiating Rathke cleft cysts from pure cystic pituitary adenoma and pure cystic craniopharyngioma.

Materials And Methods: 109 patients were included in this study (56 Rathke cleft cysts, 38 pituitary adenomas, and 15 craniopharyngiomas).