Cross-Domain Fine-Tuning Effects on Foundation Models for Medical Image Segmentation

This report synthesises findings from 13 peer-reviewed papers addressing the following research question: To what extent does cross-domain fine-tuning affect the performance of foundation models like MedSAM on medical image segmentation, as measured by Dice scores across different modalities and disease. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: To what extent does cross-domain fine-tuning affect the performance of foundation models like MedSAM on medical image segmentation, as measured by Dice scores across different modalities and disease types?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.