ArticleNature2026
Merlin: a computed tomography vision-language foundation model and dataset.
Article in Nature, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Verification of the impact of differences between objective and subjective evaluation methods on the interpretation of artificial intelligence systems generating mammogram reports using vision-language model.Japanese journal of radiology · 2026Article
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- A multimodal murmuration for immunotherapy.Nature medicine · 2026Article
- Confounder-Matched Deep Learning on Cardiac CT for the Diagnosis of Tetralogy of Fallot: A Proof of Concept.Diagnostics (Basel, Switzerland) · 2026Article
- Systematic evaluation of foundation models for organ-level classification on CT scans.International journal of computer assisted radiology and surgery · 2026Article
- Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosis.Patterns (New York, N.Y.) · 2026Article
- A foundation model for acute abdomen diagnosis stratification and triage on noncontrast computed tomography.Nature communications · 2026Article
- PatchChestCT: A patch-level spatial annotation dataset for nine abnormalities in chest CT.Scientific data · 2026Article
- Handling missing data: AI approach for survival prediction in lung cancer despite missing data.NPJ digital medicine · 2026Article
- Large-Scale Multi-Cancer Detection by Learning Segmentation from Reports.Research square · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Whole body CT attenuation and volume charts from routine clinical scans via LLM report filtering.NPJ digital medicine · 2026Article
- Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.NPJ digital medicine · 2026Article
- Reimagining machine vision with optical computing.Nature · 2026Article
- A disease-centric vision-language foundation model for precision oncology in kidney cancer.Nature communications · 2026Article
- Application of Sparse Autoencoders to Enhance Mechanistic Interpretability of Large Language Models in Medicine.JMIR AI · 2026Article
- Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- AI-driven smart patient retrieval for precision oncology.Nature reviews. Cancer · 2026Article
- A generalizable deep learning system for cardiac MRI.Nature biomedical engineering · 2026Article
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
Corrections and comments
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Authors and funding
40 authors.
Funding
Abstract
The large volume of abdominal computed tomography (CT) scans
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.