The intersection of artificial intelligence, machine learning, and tissue science is reshaping how biological data is interpreted and applied. AI and big data in tissue engineering enable unprecedented levels of precision in designing scaffolds, selecting cell sources, and predicting regenerative outcomes. Through pattern recognition and algorithmic modeling, AI systems can process vast volumes of multi-omics and imaging data, revealing correlations invisible to human analysis. Researchers now use predictive analytics to simulate cellular behavior, refine bioprinting strategies, and anticipate immune responses to implants. By integrating clinical and preclinical data, AI-driven platforms are also helping accelerate patient stratification and personalized therapeutic design. AI and Big Data in Tissue Engineering represent not just technological advancement, but a paradigm shift in the way research is conducted and therapies are developed—providing a foundation for adaptive, data-informed regenerative solutions that continuously evolve based on real-world performance.
Title : Eliminating implants infections with nanomedicine: Human results
Thomas J Webster, Interstellar Therapeutics, United States
Title : Biodistribution and gene targeting in regenerative medicine
Nagy Habib, Imperial College London, United Kingdom
Title : Graphene, butterfly structures, and stem cells: A revolution in surgical implants
Alexander Seifalian, Nanotechnology & Regenerative Medicine Commercialisation Centre, London NW1 0NH, United Kingdom
Title : Precision in cartilage repair: Breakthroughs in biofabrication process optimization
Pedro Morouco, Polytechnic of Leiria, Portugal
Title : Keratin-TMAO wound dressing promote tissue recovery in diabetic rats via activation of M2 macrophages
Marek Konop, Medical University of Warsaw, Poland
Title : Assessing geometric simplifications in vertebral modeling for reliable numerical analysis of intervertebral discs
Oleg Ardatov, Vilnius University, Lithuania