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Clinical artificial intelligence applications of vision-language foundation models.
Arun James Thirunavukarasu1,2, Siyou Li3, Pengyao Qin4
1Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, United Kingdom.
PLOS Digital Health
|June 11, 2026
Summary
Vision-language models (VLMs) enhance healthcare by processing multimodal data for improved clinical decisions and efficiency. Rigorous validation and evolving regulations are key for safe and equitable AI-augmented healthcare.
Area of Science:
- Generative artificial intelligence (AI)
- Multimodal data processing
Background:
- Vision-language models (VLMs) leverage transformer architectures for advanced AI capabilities.
- VLMs demonstrate proficiency in clinical tasks such as image interpretation and report generation.
Purpose of the Study:
- To explore the transformative potential of VLMs in clinical decision-making and workflow efficiency.
- To highlight emerging applications of VLMs in radiology, pathology, and medical education.
Main Methods:
- Utilizing multimodal data processing inherent to VLM architecture.
- Focusing on applications in image interpretation, report generation, and visual question-answering.
Main Results:
- VLMs show potential in automating documentation and real-time clinical decision support.
- Emerging applications span radiology, pathology, and broader clinical practice.
Conclusions:
- Successful VLM integration necessitates validation to address bias, interpretability, and safety.
- Prospective trials, economic evaluations, and stakeholder engagement are crucial for equitable deployment.
- Evolving regulatory frameworks are essential for accountability and patient safety in AI-augmented healthcare.
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