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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Fine-Tuned Large Language Model for Automated Radiology Impression Generation: A Multicenter Evaluation.

Mingyang Li1, Yaning Wang1, Zheng Miao1

  • 1Department of Radiology, The First Hospital of Jilin University, No. 79 Xinmin St, Changchun 130021, China.

Radiology. Artificial Intelligence
|April 15, 2026
PubMed
Summary
This summary is machine-generated.

A new large language model, MIRA, was developed to generate radiology impressions, showing improved accuracy and efficiency in multicenter settings. This AI tool enhances reporting consistency and reduces drafting time for radiologists.

Keywords:
CADComputer Applications—General InformaticsComputer-aided DiagnosisConventional RadiographyMRIStatisticsSupervised LearningTransfer Learning

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Area of Science:

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

Background:

  • Radiology reporting is crucial for patient care.
  • Current reporting methods can be time-consuming and prone to variability.
  • Large language models (LLMs) offer potential for automating and improving report generation.

Purpose of the Study:

  • To develop and evaluate a fine-tuned LLM, the Medical Imaging Report Assistant (MIRA).
  • To assess MIRA's performance in generating radiology impressions from multicenter data.
  • To measure MIRA's accuracy, reporting efficiency, and clinical applicability compared to existing models.

Main Methods:

  • A retrospective multicenter dataset of 1.87 million radiology reports was compiled.
  • A large language model was fine-tuned using a prompt-based strategy.
  • Performance was evaluated using automated metrics and blinded comparisons by 24 radiologists.

Main Results:

  • Site/modality-aware prompting significantly improved similarity scores (BERTScore-F/Sentence Similarity: 0.92/0.92 internally, 0.82/0.80 externally).
  • Human evaluation showed MIRA outperformed GPT-4o in similarity and F1 score (P < .001).
  • MIRA-generated impressions were rated as good as reference impressions in 69.0% of cases, reducing draft time by 0.46 min and increasing interradiologist agreement (P < .001).

Conclusions:

  • MIRA, a fine-tuned LLM, effectively generates clinically aligned radiology impressions.
  • The model demonstrates improved accuracy, efficiency, and reporting consistency in multicenter settings.
  • MIRA shows significant potential to enhance the radiology reporting workflow.