Artificial intelligence and machine learning-driven design of self-healing biomedical composites.

Abstract

Introduction: The integration of Artificial Intelligence (AI) and Machine Learning (ML) has significantly enhanced the development of self-healing composites, especially in biomedical fields including tissue engineering, medication delivery, and implantable devices. These materials are designed to self-repair damage, enhancing durability, patient safety, and operational reliability. Considering that traditional materials may deteriorate under physiological conditions, intelligent self-healing composites augmented by AI/ML offer a revolutionary alternative.

Areas covered: This work examines current progress in AI- and ML-facilitated design, selection, and optimization of self-healing composites for biomedical applications. Attention is directed toward the application of supervised and unsupervised learning methodologies - such as Bayesian optimization, neural networks, and support vector machines to improve healing efficiency by 30-50%, decrease formulation time by approximately 40%, and achieve predictive accuracies of over 90% regarding failure or healing behavior in specific studies.

Expert opinion: The research examines ethical aspects, encompassing data protection, algorithmic transparency, and adherence to regulatory standards such as FDA and ISO 10,993. The paper emphasizes the transformational potential of AI/ML in facilitating intelligent, responsive, and patient-specific composite designs, while also addressing possible issues such as dataset bias and algorithmic opacity. The results indicate that AI-enhanced self-healing systems will be pivotal in the future of customized medicine.