ArticleNature communications2025
Medical multimodal multitask foundation model for lung cancer screening.
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 34 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
34 citing papers in PubMed.
- Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma.PLOS digital health · 2026Article
- Understanding and Mitigating Distribution Shifts in Volumetric Lung Nodule CAD Using a 3D Vision-Language Framework.Journal of imaging informatics in medicine · 2026Article
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT.IEEE transactions on medical imaging · 2026Article
- Review
- Foundation model for screening severe mitral regurgitation and severe aortic stenosis from coronary angiograms.Visual computing for industry, biomedicine, and art · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Clinical usefulness of polygenic risk scores in risk prediction models for lung cancer screening and lung nodule management.Translational oncology · 2026Article
- Applying artificial intelligence to ensure high quality and equitable lung cancer screening.Translational lung cancer research · 2026Review
- Advances in translational lung cancer research in 2025: a narrative review.Translational lung cancer research · 2026Review
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- The current and future landscape of AI foundation models for cancer management.Nature communications · 2026Article
- 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
- A foundation model-enhanced CT radiomics signature for the noninvasive assessment of tertiary lymphoid structures and prediction of therapy benefit in gastric cancer.Cancer biology & medicine · 2026Article
- Graphicalized vision-language modeling for comprehensive lung nodule analysis and risk stratification.NPJ digital medicine · 2026Article
- Article
- Automated diagnostic of cervical spondylosis on multimodal medical images with a multi-task deep learning model.Nature communications · 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Lung cancer screening (LCS) reduces mortality and involves vast multimodal data such as text, tables, and images. Fully mining such big data requires multitasking; otherwise, occult but important features may be overlooked, adversely affecting clinical management and healthcare quality. Here we propose a medical multimodal-multitask foundation model (M3FM) for three-dimensional low-dose computed tomography (CT) LCS. After curating a multimodal multitask dataset of 49 clinical data types, 163,725 chest CT series, and 17 tasks involved in LCS, we develop a scalable multimodal question-answering model architecture for synergistic multimodal multitasking. M3FM consistently outperforms the state-of-the-art models, improving lung cancer risk and cardiovascular disease mortality risk prediction by up to 20% and 10% respectively. M3FM processes multiscale high-dimensional images, handles various combinations of multimodal data, identifies informative data elements, and adapts to out-of-distribution tasks with minimal data. In this work, we show that M3FM advances various LCS tasks through large-scale multimodal and multitask learning.
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.