Hydrogels and artificial intelligence (AI) in tissue engineering and regenerative medicine (TERM): an update on ‘skin and bones

This research review explores the convergence of 3D-printed hydrogels and artificial intelligence (AI) within tissue engineering and regenerative medicine (TERM), specifically focusing on skin and bone regeneration. Traditional scaffold design for musculoskeletal and dermatological applications often relies on iterative, trial-and-error experimental cycles to optimize mechanical properties, degradation rates, and biocompatibility. Integrating AI methodologies—such as machine learning regression models and neural networks—offers a systematic approach to predicting hydrogel printability, structural integrity, and cellular viability based on bioink composition and printing parameters. The source paper highlights how computational models can analyze complex multi-variable datasets to accelerate the development of personalized, minimally invasive implants. However, because the source material is a high-level review,

specific experimental datasets and proprietary algorithmic architectures are not fully detailed. For M.Tech research direction, this work provides a foundation for structuring simulation studies that model shear stress, gelation kinetics, and mechanical behavior of composite hydrogels. By leveraging AI-driven optimization, researchers can systematically evaluate the trade-offs between scaffold degradation and tissue neo-formation, bridging the gap between computational material design and functional tissue regeneration.

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