Anomaly detection in medical via multimodal foundation models
Additionally, it improves interpretability and trustworthiness, which are critical for clinical deployment. By aligning domain knowledge with multimodal AI, our work contributes a generalizable and explainable solution to healthcare anomaly detection.
Authors
- Zhong Tang (ORCID: https://orcid.org/0000-0001-8355-7493)
- Zhenmin Tang (ORCID: https://orcid.org/0000-0001-6708-2205)
- Jing Wu (ORCID: https://orcid.org/0009-0007-9850-9698)
Institutions
- Xi'an University of Architecture and Technology (CN)
- Guangxi Medical University (CN)
- University of Macau (MO)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2025-08-12
- DOI
- https://doi.org/10.3389/fbioe.2025.1644697
- Citations
- 3
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 7.44