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ReXamine-Global: A Framework for Uncovering Inconsistencies in Radiology Report Generation Metrics.

Oishi Banerjee1, Agustina Saenz2, Kay Wu3

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA, oishi_banerjee@g.harvard.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 13, 2024
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Summary
This summary is machine-generated.

New AI evaluation metrics are needed for radiology reports. ReXamine-Global, a large language model (LLM)-powered framework, tests existing metrics across hospitals, revealing significant generalizability gaps.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology Report Generation and Evaluation
  • Natural Language Processing in Healthcare

Background:

  • Generative AI models for radiology are rapidly advancing, necessitating robust methods to assess AI-generated report quality.
  • Existing metrics for evaluating AI radiology reports may lack generalizability across diverse clinical settings and reporting styles.
  • A standardized, multi-site framework is crucial for validating AI report evaluation metrics.

Purpose of the Study:

  • To develop and apply a multi-site framework, ReXamine-Global, to test the generalizability of established AI radiology report evaluation metrics.
  • To assess metric sensitivity to reporting style variations and agreement with expert evaluations across different hospital sites.
  • To identify limitations in current metrics and guide the development of more robust AI report evaluation tools.

Main Methods:

  • Developed ReXamine-Global, a large language model (LLM)-powered framework for multi-site testing of AI report evaluation metrics.
  • Evaluated metric sensitivity to stylistic differences between AI-generated and ground-truth reports.
  • Measured metric agreement with expert assessments of AI-generated report quality across six international hospitals using 240 reports.

Main Results:

  • Application of ReXamine-Global to seven established metrics revealed significant gaps in their generalizability across diverse hospital settings.
  • Certain metrics showed undesirable sensitivity to reporting styles, leading to inconsistent quality scores.
  • Divergence between metric scores and expert opinions was observed at specific sites, highlighting reliability issues.

Conclusions:

  • Existing metrics for AI-generated radiology reports exhibit serious limitations in generalizability across different institutions.
  • ReXamine-Global provides a vital tool for developers to design and validate robust AI evaluation metrics.
  • The findings guide users in selecting appropriate metrics and evaluation procedures for their specific hospital contexts.