Related Experiment Video
Updated: Jul 12, 2026
09:35
Distinctive Capillary Action by Micro-channels in Bone-like Templates can Enhance Recruitment of Cells for Restoration of Large Bony Defect
Published on: September 11, 2015
Reproducibility and Validation Challenges in AI-Driven Scaffold Design for Bone Regeneration
Mohammadamin Damsaz1,2, Farnaz Heidari Laybidi3, Zohreh Moradipour4
1Research Director, Facial Plastic Surgery Research Program, iFACE Academy, Toronto, Canada.
Tissue Engineering. Part B, Reviews
|July 10, 2026
Summary
Artificial intelligence (AI) aids tissue engineering (TE) for bone regeneration but faces challenges. A new framework improves AI-TE validation and reporting for better clinical translation.
Area of Science:
- Biomaterials Science
- Tissue Engineering
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is increasingly used in bioink formulation and bioprinting for tissue engineering (TE).
- Translational progress in AI-driven TE is hindered by small datasets, limited validation, and poor links between computational predictions and biological results.
- Existing reporting standards lack TE-specific requirements for validation and translational readiness.
Purpose of the Study:
- To critically evaluate experimentally validated AI applications in scaffold-based bone regeneration.
- To identify methodological gaps and propose solutions for improving AI-TE reliability and translational relevance.
- To introduce the AI-TE reporting framework for enhanced validation and documentation.
Main Methods:
- Review of experimentally validated AI applications in bone regeneration TE.
- Analysis of AI approaches including physics-informed models and transfer learning.
- Development of a reporting framework addressing TE-specific needs.
Main Results:
- Physics-informed AI models show greater robustness and generalizability than purely data-driven methods.
- Transfer learning in AI-TE is challenging due to variability in cellular responses and fabrication.
- The proposed AI-TE framework incorporates essential elements for biological validation, fabrication documentation, and translational readiness.
Conclusions:
- AI holds significant promise for bone regeneration TE, but methodological rigor and validation are crucial.
- Addressing current gaps in reporting and validation is essential for advancing AI-TE applications.
- The AI-TE reporting framework aims to enhance reproducibility, reliability, and clinical translation of AI in bone regeneration.
