AI-driven reconstruction of the evidence architecture of hydrogel-based intervertebral disc repair research

Intervertebral disc degeneration (IVDD), the leading cause of chronic low back pain, imposes heavy socioeconomic burdens, and conventional treatments cannot achieve biological disc repair. Hydrogels are promising for IVDD regeneration, but traditional bibliometrics fails to uncover the field's evidence architecture, thematic maturity and structural bottlenecks.

We retrieved 1,085 English publications (2016-2025) from three major databases, and developed an AI-driven framework to reconstruct the field's evidence hierarchy via a five-level experimental-stage-based classification and five-dimensional thematic maturity assessment.

The field has entered the mid-to-late preclinical stage, dominated by Level 3 (animal/ex vivo) evidence, with only 7 Level 5 (advanced translational) studies. Research focus has shifted to Level 4 (degeneration/mechanism) validation. Three mature pillars were identified: anti-inflammation/microenvironment regulation, ECM biomimetic hydrogels and nucleus pulposus regeneration. Annulus fibrosus repair is the critical bottleneck, while mechanical functional restoration and composite/multifunctional hydrogels are the most promising emerging directions.

This AI-assisted evidence reconstruction maps the unbalanced transition of the field from biomaterial-centered exploration to degeneration-context integration and functional restoration. Future progress will rely on deeper structural, mechanistic and functional integration rather than expanding isolated material platforms, providing guidance for research prioritization and clinical translation.