From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties
Felix Busch, Tianyu Han, Marcus Makowski, Daniel Truhn, Keno Bressem, Lisa Adams
TL;DR
This paper investigates GPT-4Vision's ability to analyze radiological images, demonstrating its potential to improve diagnostic accuracy across various subspecialties compared to text-only models.
Contribution
It introduces GPT-4Vision as a multimodal model capable of recognizing radiological features directly from images, advancing AI applications in medical diagnostics.
Findings
GPT-4Vision can recognize radiological features from images.
It outperforms text-based GPT-4 in diagnostic tasks.
Potential to enhance radiological analysis across subspecialties.
Abstract
The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnostic potential over text-based descriptions.
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Radiology practices and education · Topic Modeling
MethodsAttention Is All You Need · Dense Connections · Dropout · Byte Pair Encoding · Softmax · Absolute Position Encodings · Layer Normalization · Linear Layer · Position-Wise Feed-Forward Layer · Label Smoothing