The Diagnostic Performance of Large Language Models and General Radiologists in Thoracic Radiology Cases: A Comparative Study.

Purpose: To investigate and compare the diagnostic performance of 10 different large language models (LLMs) and 2 board-certified general radiologists in thoracic radiology cases published by The Society of Thoracic Radiology.

Materials And Methods: We collected publicly available 124 "Case of the Month" from the Society of Thoracic Radiology website between March 2012 and December 2023. Medical history and imaging findings were input into LLMs for diagnosis and differential diagnosis, while radiologists independently visually provided their assessments.

Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5 edition.

Purpose: This study aimed to evaluate the performance of large language models (LLMs) and multimodal LLMs in interpreting the Breast Imaging Reporting and Data System (BI-RADS) categories and providing clinical management recommendations for breast radiology in text-based and visual questions.

Methods: This cross-sectional observational study involved two steps. In the first step, we compared ten LLMs (namely ChatGPT 4o, ChatGPT 4, ChatGPT 3.

Can large language models be new supportive tools in coronary computed tomography angiography reporting?

The advent of large language models (LLMs) marks a transformative leap in natural language processing, offering unprecedented potential in radiology, particularly in enhancing the accuracy and efficiency of coronary artery disease (CAD) diagnosis. While previous studies have explored the capabilities of specific LLMs like ChatGPT in cardiac imaging, a comprehensive evaluation comparing multiple LLMs in the context of CAD-RADS 2.0 has been lacking.

Comparison of Performance of Large Language Models on Lung-RADS Related Questions.

This study evaluates LLM integration in interpreting Lung-RADS for lung cancer screening, highlighting their innovative role in enhancing radiological practice. Our findings reveal that Claude 3 Opus and Perplexity achieved a 96% accuracy rate, outperforming other models.

Optimizing Diagnostic Performance of ChatGPT: The Impact of Prompt Engineering on Thoracic Radiology Cases.

Background Recent studies have highlighted the diagnostic performance of ChatGPT 3.5 and GPT-4 in a text-based format, demonstrating their radiological knowledge across different areas. Our objective is to investigate the impact of prompt engineering on the diagnostic performance of ChatGPT 3.

Primary brain lymphoma and glioblastoma: evaluation of DCE T1 and DSC T2 MRI perfusion findings.

Background: The accurate differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma multiforme (GBM) is clinically crucial due to the different treatment strategies between them.

Purpose: To define magnetic resonance imaging (MRI) perfusion findings in PCNSL to make a safe distinction from GBM with dynamic contrast-enhanced (DCE) T1 and DSC T2 MRI perfusion findings.

Material And Methods: This retrospective analysis included 19 patients with histopathologically diagnosed PCNSL and 21 individuals with GBM.

MRI in the Diagnosis of Bucket Handle Tears: What Is the Current Situation?

Introduction The purpose of this study was to determine the utility of current magnetic resonance imaging (MRI) in the diagnosis of bucket-handle meniscal tears. Materials and methods Patients treated for arthroscopic meniscal tears between March 2019 and March 2022 were reviewed. The current study included all patients with bucket handle tears diagnosed arthroscopically and having MRI scans (n=51).