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Merlin: a computed tomography vision-language foundation model and dataset.

Louis Blankemeier1,2,3, Ashwin Kumar2,3, Joseph Paul Cohen2,3

  • 1Department of Electrical Engineering, Stanford University, Stanford, CA, USA.

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|March 4, 2026
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Summary
This summary is machine-generated.

Merlin, a novel 3D vision-language model (VLM), enhances abdominal CT scan analysis by integrating volumetric scans, electronic health records, and radiology reports. This advanced VLM demonstrates superior performance in diagnostic, prognostic, and quality tasks, aiding radiologists and biomarker discovery.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Medical Diagnostics
  • Computer Vision and Natural Language Processing

Background:

  • Increasing volume of abdominal computed tomography (CT) scans necessitates automated analysis tools due to radiologist shortages.
  • Existing vision-language models (VLMs) for medical imaging are often limited to 2D data and concise reports, hindering comprehensive abdominal CT interpretation.

Purpose of the Study:

  • To introduce Merlin, a 3D VLM designed for in-depth abdominal CT interpretation, overcoming limitations of previous 2D-based approaches.
  • To develop a VLM capable of learning from volumetric CT scans, electronic health records, and radiology reports without manual annotations.

Main Methods:

  • Developed Merlin, a 3D VLM, utilizing a multistage pretraining framework on a large clinical dataset (>6 million images, >1.8 million diagnosis codes, >6 million report tokens).
  • Evaluated Merlin on 6 task types and 752 individual tasks, including zero-shot classification, cross-modal retrieval, chronic disease prediction, report generation, and 3D semantic segmentation.
  • Validated Merlin through extensive internal (5,137 scans) and external testing (44,098 scans) across multiple institutions and public datasets.

Main Results:

  • Merlin demonstrated high generalization across institutions and anatomies, outperforming existing 2D VLMs, CT foundation models, and off-the-shelf radiology models.
  • Achieved strong performance in zero-shot classification of 30 findings and 692 phenotypes, and effective cross-modal retrieval.
  • Showcased proficiency in adapted tasks, including 5-year chronic disease prediction for 6 diseases, radiology report generation, and 3D semantic segmentation of 20 organs.

Conclusions:

  • Merlin represents a significant advancement in automated abdominal CT interpretation, assisting radiologists and mitigating workload.
  • The 3D VLM holds potential for biomarker discovery and disease risk stratification, adding value beyond diagnostic tasks.
  • The release of trained models, code, and a dataset (25,494 pairs) facilitates further research and development in medical AI.