ArticleNature biomedical engineering2026
Generalist foundation models from a multimodal dataset for 3D computed tomography.
Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed.
- Understanding and Mitigating Distribution Shifts in Volumetric Lung Nodule CAD Using a 3D Vision-Language Framework.Journal of imaging informatics in medicine · 2026Article
- Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosis.Patterns (New York, N.Y.) · 2026Article
- PatchChestCT: A patch-level spatial annotation dataset for nine abnormalities in chest CT.Scientific data · 2026Article
- Why Only 3D for 3D? 2D-Guided Supervision for 3D Medical Vision-Language Models.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Aortic Geometric Atlas: Centile-Based Reference Charts and Pathological Signatures Across the Adult Lifespan.medRxiv : the preprint server for health sciences · 2026Article
- Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.NPJ digital medicine · 2026Article
- Clinical Metadata-Guided Limited-Angle CT Image Reconstruction.IEEE transactions on medical imaging · 2026Article
- Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.NPJ digital medicine · 2026Article
- FetalCLIP: a visual-language foundation model for fetal ultrasound image analysis.NPJ digital medicine · 2026Article
- A disease-centric vision-language foundation model for precision oncology in kidney cancer.Nature communications · 2026Article
- A universal foundation model for grounded biomedical image interpretation.Nature communications · 2026Article
- Clinical artificial intelligence applications of vision-language foundation models.PLOS digital health · 2026Review
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- OpenVocabCT: Toward Universal Text-Driven CT Image Segmentation.IEEE transactions on medical imaging · 2026Article
- Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.Journal of medical Internet research · 2026Article
- Article
- Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.Briefings in bioinformatics · 2026Review
- A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.Patterns (New York, N.Y.) · 2026Article
- A computationally frugal, open-source chest CT foundation model for thoracic disease detection in lung cancer screening programmes.Communications medicine · 2026Article
Corrections and comments
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Authors and funding
30 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3D medical images with corresponding textual reports. CT-RATE comprises 25,692 non-contrast 3D chest CT scans from 21,304 unique patients. Each scan is accompanied by its corresponding radiology report. Leveraging CT-RATE, we develop CT-CLIP, a CT-focused contrastive language-image pretraining framework designed for broad applications without the need for task-specific training. We demonstrate how CT-CLIP can be used in multi-abnormality detection and case retrieval, and outperforms state-of-the-art fully supervised models across all key metrics. By combining CT-CLIP's vision encoder with a pretrained large language model, we create CT-CHAT, a vision-language foundational chat model for 3D chest CT volumes. Fine-tuned on over 2.7 million question-answer pairs derived from the CT-RATE dataset, CT-CHAT underscores the necessity for specialized methods in 3D medical imaging. Collectively, the open-source release of CT-RATE, CT-CLIP and CT-CHAT not only addresses critical challenges in 3D medical imaging but also lays the groundwork for future innovations in medical AI and improved patient care.
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Identifiers
41680439What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.