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
解鎖 BiomedCLIP 全部潛力
獲取先進醫學圖像-文字理解的無限訪問權限，享受增強功能、優先支援和全面的多模態分析能力。
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
應用場景
醫學圖像搜索
使用自然語言文字查詢實現醫學圖像的高效檢索，支援快速診斷。
圖像分類
利用零樣本學習能力，高精度自動分類醫學圖像。
醫學研究
透過文獻分析、假設生成和數據解釋加速醫學研究。