This research introduces a novel agent-based collaboration framework that combines domain-specific and general-purpose models to enhance image captioning, especially for medical applications.

• Leverages specialized medical knowledge and general language capabilities
• Creates more accurate and contextually rich diagnostic reports
• Addresses limitations of single-model approaches through intelligent collaboration
• Demonstrates improved performance in medical image interpretation

This innovation matters for healthcare because it produces more comprehensive and accurate diagnostic reports from medical images, potentially improving clinical decision-making and reducing interpretation errors.

MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning