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IHRAS: Automated Medical Report Generation from Chest X-Rays via Classification, Segmentation, and LLMs.

Gabriel Arquelau Pimenta Rodrigues1, André Luiz Marques Serrano1,2, Guilherme Dantas Bispo1

  • 1Department of Electrical Engineering, University of Brasilia, Federal District, Brasília 70910-900, Brazil.

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Summary
This summary is machine-generated.

This study introduces the Intelligent Humanized Radiology Analysis System (IHRAS) for automated Chest X-Ray (CXR) analysis and reporting. The AI system accurately interprets thoracic conditions and generates structured reports, aiding radiologists.

Keywords:
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Clinical Informatics

Background:

  • Increasing demand for efficient and accurate Chest X-Ray (CXR) interpretation.
  • Need for AI solutions to reduce radiologist workload and diagnostic variability.
  • Lack of standardized, interpretable AI tools in radiological workflows.

Purpose of the Study:

  • To introduce the Intelligent Humanized Radiology Analysis System (IHRAS), an AI framework for automated CXR analysis and report generation.
  • To evaluate the diagnostic performance and report quality of IHRAS.
  • To demonstrate a transparent and scalable AI solution for supporting radiological workflows.

Main Methods:

  • Development of IHRAS, a modular framework integrating deep convolutional neural networks for classification, Grad-CAM for visualization, SAR-Net for segmentation, and a large language model (DeepSeek-R1) for report generation.
  • Utilized the CRISPE prompt engineering framework for report generation with SNOMED CT terminology.
  • Evaluated on the NIH ChestX-ray dataset, assessing diagnostic performance and report quality metrics (faithfulness, relevancy, alignment).

Main Results:

  • IHRAS demonstrated consistent diagnostic performance across diverse demographic and clinical subgroups.
  • The system generated high-fidelity, clinically relevant radiological reports.
  • Report quality scores for faithfulness, relevancy, and alignment were strong.

Conclusions:

  • IHRAS provides an automated, end-to-end solution for CXR analysis and report generation.
  • The system offers a transparent and scalable approach to support radiological workflows.
  • Highlights the importance of interpretability and standardization in clinical AI applications.