Academic Journal
Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions.
| Τίτλος: | Medical Vision-Language Models: Existing Technologies, Clinical Applications and Future Directions. |
|---|---|
| Συγγραφείς: | Zou L; College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha 410073, China., Ma M; College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha 410073, China., Li J; College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha 410073, China., Chen H; College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha 410073, China., Peng S; College of Electronic Science and Technology, National University of Defense Technology, No. 109 Deya Road, Kaifu District, Changsha 410073, China. |
| Πηγή: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 Jun 24; Vol. 26 (13). Date of Electronic Publication: 2026 Jun 24. |
| Τύπος έκδοσης: | Journal Article; Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI, c2000- |
| Ιατρικοί όροι (MeSH): | Image Processing, Computer-Assisted*/methods , Language*, Humans ; Deep Learning |
| Περίληψη: | Medical image analysis is a cornerstone of modern healthcare, yet conventional single-modal deep learning often struggles with the unique physical constraints and structural variability inherent in data acquired from diverse medical sensors. Recently, Vision-Language Models (VLMs) have sparked a paradigm shift by bridging the semantic gap between visual sensor signals and clinical narratives. Following the PRISMA guidelines, 167 representative studies are systematically synthesized in this review to provide a comprehensive roadmap of VLM technological evolution and clinical utility. First, rather than treating VLMs as generic feature extractors, their underlying mechanisms are uniquely distilled into seven core operational principles, which are then explicitly mapped to downstream applications such as few-shot diagnosis, prompt-driven segmentation, and multi-task foundation models. To facilitate intuitive evaluation, a rigorous quantitative cross-comparison of current benchmark architectures is presented. Crucially, this review goes beyond highlighting successes by critically assessing prevalent clinical bottlenecks, including zero-shot segmentation failures, multi-modal hallucinations in diagnosing rare diseases, and the prohibitive computational complexity associated with 3D volumes and gigapixel whole slide images. Finally, a novel, forward-looking framework is proposed: the transition from static "image-text alignment" to dynamic "multi-source sensor-driven intelligence". By addressing both physical sensor constraints and algorithmic limitations, this survey offers actionable insights for developing trustworthy, sensor-aware clinical diagnostic agents. |
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| Grant Information: | No.42471403 National Natural Science Foundation of China; No.42571548 National Natural Science Foundation of China; No.42501540 National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: artificial intelligence; clinical application; medical image analysis; multi-modal learning; vision-language model |
| Entry Date(s): | Date Created: 20260715 Date Completed: 20260715 Latest Revision: 20260813 |
| Update Code: | 20260814 |
| PubMed Central ID: | PMC13364034 |
| DOI: | 10.3390/s26133998 |
| PMID: | 42451242 |
| Βάση Δεδομένων: | MEDLINE |
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