AI and big data driven knowledge mapping of exosome–hydrogel research in orthopedic regeneration and tissue engineering

Abstract

Background: Exosome-hydrogel complexes have great potential in regenerative medicine, being able to combine biological signals with structural support. But overall, the knowledge structure and translational connections between academic discoveries and patent deployment are not clear. Methods: A dual-source analysis framework was established to analyze academic papers and patents, illustrating the landscape of exosome-hydrogel research from 2016 to 2025. An interdisciplinary knowledge graph was constructed using topic modeling, entity-relation extraction, and evidence-ranking methods to quantify temporal trends, thematic differences, and translational gaps. Results: The core components include mesenchymal stem cell-derived exosomes and hydrogels based on gelatin methacrylate (GelMA) or collagen, which form a well-established research foundation. Academic research focuses on osteogenesis, and recent progress mentions angiogenesis and immune regulation. The research application has strong temperature dependence, and patent activities lag behind academic publications. Several high-evidence yet unpatented propositions, such as "hydrogel-encapsulated exosomes" and "exosome-enhanced angiogenesis," represent potential innovation opportunities. Conclusion: This study employs a data-driven framework to connect scientific research with transformation. The integration of semantic models and cross - source evidence reflects the evaluation logic of exosome - hydrogel research, and provides support for future research in the field of regenerative biomaterials and the priority of patent strategies.

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