
Latest multimodal large language models show limitations on image-based radiology exam questions.
Key Details
- 1Researchers tested ChatGPT-4v and ChatGPT-4o on 222 image-based multiple-choice questions from national radiology board exams (2020 and 2024).
- 2These LLMs have been recently trained to process both text and images.
- 3Despite advancements, significant concerns remain regarding their reliability for diagnostic tasks in radiology.
- 4The potential of such models in radiology workflows, such as report generation and diagnostic support, is still under early investigation.
Why It Matters
As large language models gain capability for image analysis, assessing their reliability is crucial for safe deployment in radiology. Failures on board-style questions highlight the need for ongoing scrutiny before clinical trust is warranted.

Source
Radiology Business
Related News

•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.

•Radiology Business
Study Highlights Limitations of AI in Prostate MRI Screening
New research points to several shortcomings in implementing AI for MRI-based prostate cancer screening.

•AuntMinnie
Multimodal LLMs Achieve High Accuracy Detecting Scoliosis on X-rays
Multimodal LLMs achieved up to 94% accuracy for scoliosis detection on spine x-rays, but struggled with lumbar stenosis on MRI.