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LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Zhifan Jiang1, Dong Yang2, Vishwesh Nath2

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Proceedings. IEEE International Symposium on Biomedical Imaging
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

A new framework, LUMEN, enhances chest X-ray interpretation using longitudinal data. This AI approach improves diagnostic and prognostic capabilities for radiologists, aiding clinical decision-making.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Radiology Decision Support

Background:

  • Large vision-language models (VLMs) show promise for clinical applications, particularly in radiology decision support.
  • Analyzing longitudinal imaging data (e.g., chest X-rays - CXR) is crucial for accurate diagnosis and prognosis but is time-consuming.
  • Existing VQA interfaces lack specialized optimization for longitudinal CXR interpretation.

Purpose of the Study:

  • To introduce LUMEN, a novel training framework for longitudinal CXR interpretation.
  • To enhance prognostic and diagnostic performance using multi-image and multi-task instruction fine-tuning.
  • To develop and evaluate a prognostic VQA task using a novel longitudinal instruction-following dataset.

Main Methods:

  • Developed LUMEN, a training framework optimized for longitudinal CXR interpretation.
  • Employed multi-image and multi-task instruction fine-tuning.
  • Created a new instruction-following dataset for prognostic VQA tasks, using MIMIC-CXR and Medical-Diff-VQA datasets.

Main Results:

  • LUMEN demonstrated significant improvements over baseline models in diagnostic VQA tasks.
  • The framework showed promising potential for prognostic capabilities in CXR analysis.
  • The novel longitudinal dataset enabled the development of a prognostic VQA task.

Conclusions:

  • Well-designed, instruction-tuned VLMs offer significant value in radiological interpretation.
  • LUMEN enhances the accuracy and clinical meaningfulness of interpreting longitudinal radiological imaging.
  • This work advances AI-driven decision support in clinical radiology, particularly for temporal analysis.