ArticleBioengineering (Basel, Switzerland)2023
Vision-Language Model for Visual Question Answering in Medical Imagery.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- A multitask framework for automated multi-frame right upper quadrant ultrasound interpretation and clinical decision support.Nature communications · 2026Article
- Performance of multimodal large language models on image-based surgical anatomy, anatomical pathology, and radiology questions.Anatomical sciences education · 2026Article
- DJournal of imaging · 2026Article
- Research on the application of LLaVA model based on QLoRA fine-tuning in medical teaching.PloS one · 2026Article
- Artificial intelligence for immunotherapy response assessment in lung cancer using PET/CT reports.Japanese journal of radiology · 2025Article
- Towards generalist foundation model for radiology by leveraging web-scale 2D&3D medical data.Nature communications · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review.Frontiers in surgery · 2025Review
- Evaluation and mitigation of the limitations of large language models in clinical decision-making.Nature medicine · 2024Article
- Collaborative Modality Fusion for Mitigating Language Bias in Visual Question Answering.Journal of imaging · 2024Article
- Vision-language models for medical report generation and visual question answering: a review.Frontiers in artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In the clinical and healthcare domains, medical images play a critical role. A mature medical visual question answering system (VQA) can improve diagnosis by answering clinical questions presented with a medical image. Despite its enormous potential in the healthcare industry and services, this technology is still in its infancy and is far from practical use. This paper introduces an approach based on a transformer encoder-decoder architecture. Specifically, we extract image features using the vision transformer (ViT) model, and we embed the question using a textual encoder transformer. Then, we concatenate the resulting visual and textual representations and feed them into a multi-modal decoder for generating the answer in an autoregressive way. In the experiments, we validate the proposed model on two VQA datasets for radiology images termed VQA-RAD and PathVQA. The model shows promising results compared to existing solutions. It yields closed and open accuracies of 84.99% and 72.97%, respectively, for VQA-RAD, and 83.86% and 62.37%, respectively, for PathVQA. Other metrics such as the BLUE score showing the alignment between the predicted and true answer sentences are also reported.
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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.