ArticleNature communications2025
A lung CT vision foundation model facilitating disease diagnosis and medical imaging.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- Language-Guided Segmentation of Medical Images: A Review of Foundation Models.Bioengineering (Basel, Switzerland) · 2026Review
- Review
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Interpretable Deep Learning with Multi-Scale CT for Predicting Occult Lymph Node Metastasis in Early-Stage NSCLC: A Multicenter Study.Journal of imaging informatics in medicine · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
- Foundation model-enhanced multimodal radiomics for predicting response to chemo-immunotherapy in advanced lung squamous cell carcinoma.Frontiers in oncology · 2026Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
The concomitant development and evolution of lung computed tomography (CT) and artificial intelligence (AI) have made non-invasive lung imaging a key component of clinical care of patients. However, the scarcity of labeled CT data and the limited generative capacity of existing models have constrained their clinical utility. Here, we present LCTfound, a large-scale vision foundation model designed to overcome these limitations. Trained on a multi-center dataset comprising 105,184 CT scans, LCTfound leverages diffusion-based pretraining and joint encoding of imaging and clinical information to support 8 tasks, including CT enhancement, virtual computed tomography angiography (CTA), sparse-view reconstruction, lesion segmentation, diagnosis, prognosis, cancer pathological response prediction, and three-dimensional surgical navigation. In comprehensive multicenter evaluations, LCTfound consistently outperforms leading baseline models, delivering a unified, broadly deployable solution that both augments clinical decision-making and elevates CT image quality across diverse practice settings. LCTfound establishes a scalable foundation for next-generation clinical imaging intelligence, uniting large AI model with precision healthcare.
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.