Stanford researchers introduce Merlin, a 3D vision-language AI model for interpreting abdominal CT scans, demonstrating strong performance across multiple radiology tasks.
Key Details
- 1Merlin is a 3D vision-language model designed for abdominal CT interpretation.
- 2Evaluated on over 44,000 CT scans from multiple sites covering various anatomies.
- 3Trained on over 6 million CT images, ~2 million diagnosis codes, and 6 million radiology report tokens.
- 4Tested on 752 individual tasks: zero-shot classification, disease risk prediction, cross-modal retrieval, report generation, and 3D organ segmentation.
- 5Achieved 0.741 F1 score for zero-shot classification and AUROC of 0.757 for chronic disease risk prediction over five years.
- 6Model and code are publicly available on GitHub, HuggingFace, and PyPI.
Why It Matters
This foundation model provides a significant step forward in automating CT scan interpretation, potentially alleviating radiologist shortages and expediting workflow. Its public release allows the broader research community to build and benchmark further advances.
Source
AuntMinnie
Related News
•AuntMinnie
AI Model Uses Ultrasound to Assess Fetal Lung Maturity
Researchers demonstrated an AI model's strong accuracy in measuring fetal lung maturity from ultrasound images.
•AuntMinnie
AI Model Predicts Dosimetry for Lu-177 PSMA Therapy Using PET/CT
A machine learning PET/CT model shows promise for predicting radiation dose prior to Lu-177 PSMA therapy in prostate cancer patients.
•AuntMinnie
AI Advances in Ultrasound Highlighted at AIUM 2026 Keynote
AI is increasingly enhancing ultrasound imaging, clinical workflows, and education, though challenges in trust and implementation remain.