Large language model for medical images: a survey of taxonomy, systematic review, and future trends
Open accessAbstract
The advent of Large Language Models (LLMs) has sparked considerable interest in the medical image domain, as they can generalize to multiple tasks and offer outstanding performance. While LLMs achieve promising results, there is currently a lack of a comprehensive summary of medical images, making it challenging for researchers to understand the progress within this domain. To fill this gap, we make the first attempt to present a comprehensive survey for LLM on medical images. In addition, to better summarize the current progress comprehensively, we further introduce a novel x-stage tuning paradigm for summarization, including zero-stage tuning, one-stage tuning, and multi-stage tuning, offering a unified perspective on LLMs for medical images. Finally, we discuss challenges and future directions in this domain, aiming to spur more breakthroughs in the future. We hope this work can pave the way for the broad application of LLMs in medical images and provide a valuable resource for this domain.
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Attention-based explanations were reported in 2 studies.
“Lastly, attention-based explanations were reported in 2 studies, where interpretability was derived either through direct visualization of attention weights [113] or through attention rollout-based aggregation of attention across layers [57].”
From Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review (2026) · cites this paper as [113]
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Self-citationFusion-Augmented Large Language Models: Boosting Diagnostic Trustworthiness via Model Consensus
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Rate-adaptive RDMA congestion control for AI clusters
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- Authors
- Wang, P., Lu, W., Lu, C., Zhou, R., Li, M., & Qin, L.
- Affiliations
- Central South University (CN)
- Qilu University of Technology (Shandong Academy of Sciences),Key Laboratory of Computing Power Network and Information Security Affiliated with Ministry of Education,Jinan,China,250353
- School of Computer Science and Engineering, Central South University,Changsha,China,410083
- Venue
- Big Data Mining and Analytics
- Publisher
- Tsinghua University Press
- Published
- 2025-04
- Volume
- 8
- Issue
- 2
- Pages
- 496-517
- ISSN
- 2096-0654 (ISSN-L)
- 2097-406X
- OpenAlex
- W4406890298
- Cited by
- 21
- References
- 101
- Citations by year
- 2026: 14 · 2025: 7
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Big Data Mining and Analytics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- large language models, medical images
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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