Large Language Models in Medical Image Analysis: Progress, Challenges, and Perspectives
A Special Issue of Journal of Imaging (ISSN 2313-433X) belonging to the section "Medical Imaging".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 436
Editor
Special Issue Information
Dear Colleagues,
Large language models (LLMs) have recently attracted considerable attention for their strong capabilities in representation learning, reasoning, and multimodal interaction. Beyond their success in natural language processing, LLMs are increasingly being integrated into medical image analysis frameworks, often in combination with deep learning-based vision models. This integration enables advanced imaging tasks, including image interpretation, report generation, segmentation support, visual question answering, and clinical decision support.
Despite this progress, the adoption of LLMs in medical imaging analysis poses significant challenges. Issues related to data scarcity, domain shift, hallucination, interpretability, privacy, bias, and regulatory compliance remain critical barriers to clinical translation. Moreover, the reliability and robustness of LLM-driven systems in safety-critical medical settings demand rigorous validation and benchmarking.
This Special Issue aims to provide a comprehensive overview of recent developments at the intersection of LLMs and medical image analysis. We welcome contributions that explore novel architectures, multimodal learning strategies, prompt engineering, evaluation methodologies, and clinical applications, as well as studies that critically examine limitations, ethical considerations, and future research directions. By bringing together methodological advances and practical insights, this Special Issue seeks to foster responsible innovation and accelerate the integration of LLMs into real-world medical imaging workflows.
Dr. Leibo Liu
Guest Editor
Manuscript Submission Information
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Keywords
- large language models
- medical image analysis
- multimodal learning
- vision language models
- visual question answering
- automated report generation
- clinical decision support
- model interpretability
- trustworthy AI
- AI ethics and bias
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