ChatGPT-4o and ChatGPT-5 matched or surpassed nuclear medicine experts in diagnosing neurodegenerative diseases using textual FDG-PET/CT scan descriptions.
Key Details
- 1University of Cologne team tested ChatGPT-4o and ChatGPT-5 on 100 F-18 FDG-PET/CT brain scan reports.
- 2Models achieved median diagnostic agreement scores of 1.00 against expert interpretations.
- 3ChatGPT-4o identified the correct main diagnosis in 86% of cases, ChatGPT-5 in 89%.
- 4Performance was highest in typical cases (e.g., Alzheimer's disease), lower in complex or atypical presentations.
- 5No imaging data or specific fine-tuning was used; models relied on general training.
- 6Reproducibility from run to run was 75% for ChatGPT-4o and 55% for ChatGPT-5 in a subset.
Why It Matters
This study suggests that large language models can align with expert nuclear medicine interpretation based solely on textual imaging descriptions, potentially augmenting workflow efficiency and consistency in neuroimaging. It highlights opportunities and limitations for LLMs in aiding diagnostic processes, especially when combined with automated image analysis.
Source
AuntMinnie
Related News
•Radiology Business
Radiologist-AI Collaboration Boosts Pulmonary Embolism Detection on CTPA
Combining AI with radiologists substantially improves detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) scans, new research shows.
•AuntMinnie
MRI Foundation Models and Standardization Featured at ISMRM 2026
Researchers at ISMRM 2026 presented advances in MRI AI foundation models, emphasizing standardization, multimodal analysis, and the promising OmniMRI framework.
•AuntMinnie
Deep Learning Model Predicts Brain Tumor MRI Enhancement Without Gadolinium
German researchers developed a deep learning approach to predict MRI contrast enhancement in brain tumors without the need for gadolinium-based agents.