ArticleNPJ digital medicine2025
Vision-language model for report generation and outcome prediction in CT pulmonary angiogram.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- A multitask framework for automated multi-frame right upper quadrant ultrasound interpretation and clinical decision support.Nature communications · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Do Accuracy Gains Reflect Genuine Visual Understanding? A Multi-Model Evaluation of Vision-Language Models in Thoracic Imaging.Journal of imaging informatics in medicine · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- Integrating large language models for enhanced predictive analytics in healthcare.NPJ digital medicine · 2026Article
- Uncover This Tech Term: Large Vision-Language Models in Radiology.Korean journal of radiology · 2026Article
- Lightweight hybrid foundation model for lung cancer prognosis based on low-dose chest X-ray images.Translational lung cancer research · 2026Article
- Review
- Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.Scientific reports · 2026Article
- External validation of a commercial AI system for pulmonary embolism detection on chest CTPA: a multicenter study.Frontiers in molecular biosciences · 2026Article
- Development and internal validation of a task-conditioned multimodal radiology-pathology model for breast cancer staging and biomarker profiling: a retrospective cohort study.Frontiers in cell and developmental biology · 2026Article
- AI Agents in Clinical Medicine: A Systematic Review.medRxiv : the preprint server for health sciences · 2025Article
- PathVLM-Eval: Evaluation of open vision language models in histopathology.Journal of pathology informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
Abstract
Accurate and comprehensive interpretation of pulmonary embolism (PE) from Computed Tomography Pulmonary Angiography (CTPA) scans remains a clinical challenge due to the limited specificity and structure of existing AI tools. We propose an agent-based framework that integrates Vision-Language Models (VLMs) for detecting 32 PE-related abnormalities and Large Language Models (LLMs) for structured report generation. Trained on over 69,000 CTPA studies from 24,890 patients across Brown University Health (BUH), Johns Hopkins University (JHU), and the INSPECT dataset from Stanford, the model demonstrates strong performance in abnormality classification and report generation. For abnormality classification, it achieved AUROC scores of 0.788 (BUH), 0.754 (INSPECT), and 0.710 (JHU), with corresponding BERT-F1 scores of 0.891, 0.829, and 0.842. The abnormality-guided reporting strategy consistently outperformed the organ-based and holistic captioning baselines. For survival prediction, a multimodal fusion model that incorporates imaging, clinical variables, diagnostic outputs, and generated reports achieved concordance indices of 0.863 (BUH) and 0.731 (JHU), outperforming traditional PESI scores. This framework provides a clinically meaningful and interpretable solution for end-to-end PE diagnosis, structured reporting, and outcome prediction.
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.