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Updated: Jan 9, 2026

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Group-wise Compression and Summarization via LLM-based Ensemble for Chest X-ray Report Generation.

Sang-Jun Park, Keun-Soo Heo, Bogyeong Kang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel AI method for generating accurate chest X-ray reports. The approach uses a two-step LLM ensemble and disease-based retrieval to improve diagnostic reliability and reduce radiologist workload.

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    Area of Science:

    • Medical Imaging Analysis
    • Artificial Intelligence in Healthcare
    • Natural Language Processing for Clinical Documentation

    Background:

    • Chest X-ray reports are crucial for diagnosing medical conditions but manual creation is time-consuming.
    • Accurate interpretation and clinical consistency are vital in radiology report generation.
    • Automating report generation can significantly aid radiologists and improve efficiency.

    Purpose of the Study:

    • To develop and evaluate a novel framework for automated chest X-ray report generation.
    • To enhance the accuracy, reliability, and clinical relevance of AI-generated radiology reports.
    • To reduce the manual workload for radiologists in creating chest X-ray reports.

    Main Methods:

    • A two-step Large Language Model (LLM) ensemble approach combined with disease-based retrieval.
    • Image-text embedding space similarity for initial report retrieval.
    • Filtering retrieved reports using patient-specific disease information for clinical relevance.
    • Progressive summarization by the LLM ensemble to refine reports and preserve diagnostic insights.

    Main Results:

    • The proposed framework demonstrated superior performance on MIMIC-CXR and IU X-ray benchmarks.
    • Achieved improved clinical relevance and diagnostic reliability in automated report generation.
    • Effectively reduced redundancy and enhanced coherence in the generated reports.
    • Showcased significant potential in reducing radiologist workload while maintaining report quality.

    Conclusions:

    • The novel LLM ensemble with disease-based retrieval offers a promising solution for automated chest X-ray report generation.
    • This approach enhances diagnostic reliability and clinical relevance, aiding medical professionals.
    • The framework represents a significant advancement in AI-assisted medical documentation.