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Integrating language into medical visual recognition and reasoning: A survey
1The School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.
Medical Image Analysis
|March 2, 2025
Summary
Vision-Language Models (VLMs) integrate visual and textual data for medical imaging analysis. This survey explores medical VLMs, highlighting their potential to enhance diagnostic accuracy by leveraging physician knowledge from reports.
Area of Science:
- Artificial Intelligence
- Medical Imaging Analysis
- Computer Vision
- Natural Language Processing
Background:
- Vision-Language Models (VLMs) bridge visual perception and textual interpretation.
- Medical imaging often includes extensive textual reports, offering valuable context.
- Integrating textual information can significantly improve the visual understanding of AI models in medicine.
Purpose of the Study:
- To provide a comprehensive review of medical Vision-Language Models (VLMs).
- To explore how language can be exploited in medical visual tasks.
- To discuss the potential and challenges of medical VLMs for recognition and reasoning.
Main Methods:
- Review of existing literature on medical VLMs.
- Analysis of how language information is integrated into visual tasks.
- Investigation of publicly available datasets for medical VLM research.
Main Results:
- Medical VLMs show promise in enhancing visual understanding by incorporating textual data from medical reports.
- Various approaches exist for leveraging language in medical visual tasks.
- The field is rapidly evolving with increasing interest and potential applications.
Conclusions:
- Medical VLMs represent a significant advancement in AI for healthcare.
- Further research into medical VLMs is crucial to unlock their full potential in diagnosis and treatment.
- This survey provides a foundation for understanding the current landscape and future directions of medical VLMs.
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