AI-Enhanced Bioactive 3D-Printed Scaffolds for Tissue Regeneration: Innovations in Healing and Functional Additives

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ID: 312801
2025
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Abstract
Bioactive 3D-printed scaffolds have revolutionized tissue engineering and regenerative medicine by enabling precise fabrication of biomimetic structures that promote cell adhesion, proliferation, and differentiation. However, significant challenges remain, particularly in optimizing scaffold composition, bioactive additive integration, and long-term stability for clinical applications. This review provides a systematic analysis of recent advancements in AI-driven bioactive scaffolds and their role in personalized regenerative medicine. A comparative evaluation of major 3D printing techniques—Fused Deposition Modeling (FDM), Stereolithography (SLA), Selective Laser Sintering (SLS), and Direct Metal Laser Sintering (DMLS)—is presented, focusing on resolution, material compatibility, and bioactive additive incorporation. Additionally, we analyze key bioactive agents (growth factors, nanoparticles, peptides, and natural polymers) and their effects on biocompatibility, mechanical strength, and therapeutic efficacy. AI-powered optimization techniques, including machine learning-based scaffold design, computational modeling, and predictive analytics, are emerging as transformative solutions for improving scaffold architecture, drug delivery systems, and patient-specific applications. Despite significant progress, major challenges persist, including standardization in scaffold fabrication, long-term in vivo validation, and regulatory approval hurdles. Addressing these scientific and regulatory challenges is essential for the successful clinical translation of bioactive scaffolds. This review highlights the need for interdisciplinary collaboration to advance AI-assisted scaffold engineering and establish personalized treatment strategies for next-generation regenerative medicine.
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Authors Adam Rafiq Jeraj, Zulekha Zameer
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
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URL
https://jcbi.org/index.php/Main/article/view/863
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