BiomedCLIP
Medical Image-Text Understanding Model
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Experience the power of BiomedCLIP, the advanced CLIP-based medical AI model that seamlessly bridges visual and textual understanding in healthcare, enabling sophisticated multimodal medical analysis.
Try Med-PaLM 2 Interactive Demo
Experience Google's most advanced medical AI for clinical reasoning and medical question answering
解锁 BiomedCLIP 全部潜力
获取先进医学图像-文本理解的无限访问权限，享受增强功能、优先支持和全面的多模态分析能力。
What is Med-PaLM 2?
BiomedCLIP is Google's most advanced medical AI model, representing a significant breakthrough in clinical reasoning and medical question answering. Built on the powerful PaLM 2 architecture, Med-PaLM 2 has been specifically fine-tuned for medical applications.
Med-PaLM 2 demonstrates expert-level performance on medical licensing exams and shows remarkable capabilities in clinical reasoning, medical knowledge synthesis, and complex healthcare problem-solving.
As the foundation of Google's MedLM offerings, Med-PaLM 2 enables healthcare professionals and researchers to leverage cutting-edge AI for improved patient care and medical research.
Latest Development
Announced at Google I/O 2023
Continuously improved with the latest medical knowledge and clinical reasoning capabilities
Key Features
Advanced capabilities designed for medical image-text understanding
Core Capabilities
Medical image-text alignment and understanding
Zero-shot medical image classification
Biomedical visual-semantic embedding
Medical image retrieval with text queries
Cross-modal medical knowledge representation
Medical image-text similarity assessment
Biomedical multimodal search and discovery
Medical imaging content analysis and indexing
Performance Benchmarks
Med-PaLM 2 achieves state-of-the-art results on medical benchmarks
MedQA (USMLE)
86.5%US Medical Licensing Examination questions
MedMCQA
72.3%Indian medical entrance exam questions
PubMedQA
79.7%Biomedical literature question answering
MMLU Medical
91.1%Medical knowledge across multiple domains
应用场景
医学图像搜索
使用自然语言文本查询实现医学图像的高效检索，支持快速诊断。
图像分类
利用零样本学习能力，高精度自动分类医学图像。
医学研究
通过文献分析、假设生成和数据解释加速医学研究。