MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

1. 1Design an entity-aware continual pretraining framework to organize heterogeneous medical data
2. 2Implement a vision-language foundation model that can learn from diverse medical benchmarks
3. 3Fine-tune the model on specific medical tasks to achieve state-of-the-art performance
4. 4Evaluate the model on multiple capabilities to ensure its generalizability and effectiveness

AI engineers and researchers in the medical field can benefit from MedXIAOHE as it provides a comprehensive recipe for building medical multimodal large language models, enabling them to develop more accurate and effective clinical applications

💡 Entity-aware continual pretraining is a key factor in achieving state-of-the-art performance in medical multimodal large language models

🚀 MedXIAOHE: A new medical vision-language foundation model that achieves state-of-the-art performance in medical understanding and reasoning 💡