The Incremental Curriculum-Based Fine-Tuning (ICFT) framework transforms general LLMs into specialized medical AI assistants through progressive, efficient learning.

• Combines curriculum-based learning with dual-stage memory coordination
• Utilizes parameter-efficient fine-tuning techniques for medical specialization
• Enables cross-domain weak supervision to enhance model generalization
• Creates more capable medical language models for healthcare applications

This research matters because it addresses the specialized knowledge requirements in healthcare while maintaining general capabilities, potentially improving clinical decision support, patient education, and healthcare accessibility.

Generalization of Medical Large Language Models through Cross-Domain Weak Supervision