Artificial Intelligence in Collagen‐based Biomaterials

Abstract Collagen, the most abundant structural protein in animals, is central to biomedical devices, cosmetics, nutraceuticals and regenerative materials. However, traditional approaches to collagen sourcing, extraction and functionalisation are time‐consuming, empirical and increasingly inadequate in meeting the growing demand for sustainable and high‐performance collagen‐based products. This review explores how artificial intelligence (AI) is transforming collagen research and development (R&D) by enabling predictive, adaptive and data‐driven decision‐making across the full value chain, from raw material selection and extraction optimisation to characterisation, peptide discovery, imaging and product design. The review synthesises over 160 studies and technical reports, offering an application focused overview of how machine learning, deep learning and data modelling are being used to: optimise enzymatic hydrolysis protocols; predict peptide functionality for food, cosmetic and pharmaceutical applications; classify collagen materials through spectroscopic and image‐based data; and design customised formulations using in silico tools. It highlights emerging uses of AI in second harmonic generation imaging for tissue diagnostics, patient‐specific scaffold modelling in biomedicine and AI‐integrated traceability systems that support regulatory compliance and ethical sourcing. In addition to outlining successful implementations, the review addresses current challenges including data scarcity, model interpretability, computational access and ethical concerns around AI‐generated bioactive. It further discusses future directions such as digital twins, multimodal learning and federated data frameworks that could enable real‐time simulation and personalised biomaterial design. Altogether, these developments mark a major change in how collagen is studied and used, highlighting the growing role of smart technologies, clear traceability and environmentally responsible approaches. They also show how essential it is for experts from different fields to work together and for strong digital systems to support progress in collagen research.