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Enhancing radiology workflows through collaborative AI-assisted chest X-ray reporting using large vision-language

Chantal Pellegrini1,2, Ege Özsoy3,4, Florian T Gassert5

  • 1School of Computation, Information and Technology, Technical University of Munich, Munich, Germany. chantal.pellegrini@gmail.com.

Insights Into Imaging
|April 28, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) assistance in radiology reporting significantly reduced chest X-ray interpretation writing time, especially for complex cases. This AI tool improved efficiency and radiologist satisfaction without compromising report quality, suggesting practical integration into clinical workflows.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Radiology reporting is time-consuming and crucial for patient care.
  • Efficiency and satisfaction in radiology workflows are areas for improvement.
  • Artificial intelligence (AI) tools show promise in assisting medical professionals.

Purpose of the Study:

  • To evaluate AI-assisted reporting for chest X-rays.
  • To assess improvements in reporting efficiency and radiologist satisfaction.
  • To determine if AI assistance compromises report quality.

Main Methods:

  • Retrospective study with three radiologists analyzing 50 chest X-rays.
  • Comparison of reporting with and without AI assistance (large vision-language model - LVLM).
Keywords:
Artificial intelligenceChest X-rayHuman-AI-collaborationLarge language modelsRadiology reporting

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  • Evaluation of writing time, suggestion acceptance, report quality, and user satisfaction via Likert scale.

    • Main Results:

    • AI assistance reduced mean writing time by 7.80% (significant for complex cases: 18.34%).
    • Efficiency gains correlated with suggestion acceptance, user-dependent (up to 27.24%).
    • Report quality and length remained stable; radiologists rated usability highly (4.33) and desired regular use (4).

    Conclusions:

    • Collaborative AI assistance can enhance radiology reporting efficiency, particularly for complex cases.
    • AI tools are well-received by radiologists and can be integrated into workflows without quality compromise.
    • Further prospective validation is warranted for clinical implementation of AI-assisted reporting.