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Decoupling Visual Parsing and Diagnostic Reasoning for Vision-Language Models (GPT-4o and GPT-5): Analysis Using

Dae Hee Han1, Eui Jin Hwang2, Soon Ho Yoon2,3

  • 1Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.

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|December 10, 2025
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Summary
This summary is machine-generated.

Vision-language models (VLMs) struggle with visual parsing of thoracic imaging, limiting diagnostic accuracy. Providing radiologist-generated descriptions significantly improves VLM performance, highlighting the need for human expertise in AI-assisted radiology.

Keywords:
artificial intelligenceclinical reasoninglarge language modelvision-language model

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Radiology

Background:

  • Vision-language models (VLMs) show promise for analyzing radiologic images and aiding diagnosis.
  • Current VLMs exhibit suboptimal performance, hindering clinical integration.
  • Evaluating specific VLM capabilities in visual parsing and diagnostic reasoning is crucial.

Purpose of the Study:

  • To assess the distinct contributions of visual parsing and diagnostic reasoning in GPT-based VLMs for thoracic imaging diagnosis.
  • To compare VLM performance with and without radiologist-provided image descriptions.

Main Methods:

  • Retrospective analysis of 128 thoracic imaging cases from a Korean Society of Thoracic Imaging quiz platform.
  • Two VLMs (GPT-4o, GPT-5) processed cases with metadata and images, or metadata and radiologist-generated descriptions.
  • Evaluation of top-5 diagnostic accuracy, image summary quality, and diagnostic concordance with radiologist performance.

Main Results:

  • VLMs achieved higher top-5 accuracy (40.1%-59.1%) with descriptions versus images alone (15.9%-24.7%).
  • Image summary quality correlated significantly with diagnostic accuracy for both models.
  • Diagnostic concordance with expert radiologists was substantially higher when using image descriptions (78.8%-79.4%) compared to direct image input (31.6%-39.3%).

Conclusions:

  • VLMs demonstrate limited capability in visually interpreting thoracic imaging findings.
  • VLM diagnostic performance significantly improves when provided with radiologist-generated image descriptions.
  • Visual image parsing, not diagnostic reasoning, is the primary limitation for current VLMs in radiology.