Large Vision–Language Models (LVLMs) in Medical Imaging: Methods, Evaluation, and Clinical Translation
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: 30 November 2026 | Viewed by 12
Special Issue Editor
Interests: COVID-19; machine learning; biomedical imaging; radiology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence have led to the development of Large Vision–Language Models (LVLMs), which integrate visual data with natural language to enable multimodal reasoning and generation. These foundation models represent a significant advance over traditional task-specific approaches and are particularly well-suited to medical imaging, where clinical interpretation relies on the integration of images, reports, and patient context.
In diagnostic imaging, LVLMs have the potential to support automated image interpretation, report generation, cohort identification, prognostic assessment, and clinical decision support. However, challenges remain regarding model reliability, interpretability, evaluation, and real-world clinical deployment. Given the rapid growth of this field and its relevance to diagnostic medicine, a focused Special Issue in Diagnostics would provide timely and critical insights for both researchers and clinicians.
This Special Issue aims to highlight recent methodological advances, practical applications, and translational considerations of LVLMs in medical imaging and diagnostics.
The objectives are as follows:
- Present state-of-the-art LVLM methods relevant to diagnostic imaging.
- Evaluate model performance, robustness, and clinical utility.
- Discuss challenges related to interpretability, bias, and deployment.
- Provide guidance for responsible clinical translation of LVLMs.
Topics of Interest:
- Architectures and training strategies for LVLMs in medical imaging;
- Image–report alignment and multimodal representation learning;
- Evaluation of LVLMs: reliability, calibration, and hallucination;
- Clinical applications in radiology and pathology (e.g., reporting, risk stratification, decision support);
- Ethical, regulatory, and workflow considerations for diagnostic use.
This Special Issue aligns closely with the mission of Diagnostics by emphasizing innovations that improve diagnostic accuracy, interpretation, and clinical decision-making. By focusing on both methodological rigor and clinical relevance, the issue will serve as a valuable resource for advancing multimodal AI applications in diagnostic medicine.
Prof. Dr. Tim Duong
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- large vision–language models (LVLMs)
- medical imaging
- diagnostics
- AI
- radiology and pathology
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