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Related Experiment Video

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Knowledge-Guided and Reinforced Selective State Space Model for radiology report generation.

Ziyang Li1, Dedong Yang1, Rongtao Li1

  • 1School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.

Journal of Biomedical Informatics
|March 11, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new AI model for radiology reports that uses medical knowledge and a smart reward system. This improves report accuracy and quality, making AI more useful in clinical settings.

Keywords:
Knowledge graphRadiology report generationReinforcement learningState space model

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Natural Language Generation

Background:

  • Current AI models for radiology reports often act as "black boxes" with basic reward functions.
  • This limits their clinical accuracy and the linguistic quality of generated reports.

Purpose of the Study:

  • To improve the clinical accuracy and linguistic quality of automated radiology report generation.
  • To address limitations of "black box" models and simplistic reward functions.

Main Methods:

  • Proposed a novel Knowledge-Guided and Reinforced Selective State Space Model (KGR-SSM).
  • Integrated a medical knowledge graph for semantic understanding of visual features.
  • Utilized a Mamba-based encoder for high-resolution image processing.
  • Implemented a hybrid reward function optimizing NLG metrics and clinical accuracy.

Main Results:

  • KGR-SSM achieved state-of-the-art performance on IU X-ray and MIMIC-CXR datasets.
  • Demonstrated significant outperformance over existing methods in linguistic and clinical efficacy.
  • Validated the model's effectiveness across comprehensive evaluation metrics.

Conclusions:

  • Integrating structured medical knowledge and a clinically-aligned hybrid reward function enhances report accuracy and reliability.
  • The KGR-SSM framework offers a robust solution for automated radiology report generation.
  • Successfully bridged the gap between technical performance and clinical utility in AI-driven medical reporting.