Data Sheet 1_AI-driven quality enhancement of medical datasets: a closed-loop framework of synthesis, annotation, quality control, and integration.docx

This review takes “quality enhancement” as its unifying axis, integrating the four pillars of AI-driven medical dataset quality enhancement—synthetic data generation, intelligent annotation, automated quality control, and integration pipelines—and proposes a four-pillar integration framework as its core contribution.

The framework organizes the four pillars into a closed loop: synthesis and annotation are coupled through dual-track feedback (a quantity track that replenishes data and a strategy track that adjusts generation strategy), while annotation and quality control are coupled through bidirectional verification. Beyond this forward enhancement loop, the framework further employs a bias amplification chain (as a heuristic insight) and the privacy–utility tradeoff as two tension lines, and incorporates Metrics Reloaded, FAIR, ALCOA+, and YY/T 1833 as sub-layer evaluation dimensions in a complementary manner.