Senthil Maharaj Kennedy, Amudhan K, Padmapriya K, Jeen Robert Rb
{"title":"人工智能和机器学习驱动的自修复生物医学复合材料设计。","authors":"Senthil Maharaj Kennedy, Amudhan K, Padmapriya K, Jeen Robert Rb","doi":"10.1080/17434440.2025.2520291","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>","PeriodicalId":94006,"journal":{"name":"Expert review of medical devices","volume":" ","pages":"787-805"},"PeriodicalIF":2.7000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and machine learning-driven design of self-healing biomedical composites.\",\"authors\":\"Senthil Maharaj Kennedy, Amudhan K, Padmapriya K, Jeen Robert Rb\",\"doi\":\"10.1080/17434440.2025.2520291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>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.</p><p><strong>Areas covered: </strong>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.</p><p><strong>Expert opinion: </strong>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.</p>\",\"PeriodicalId\":94006,\"journal\":{\"name\":\"Expert review of medical devices\",\"volume\":\" \",\"pages\":\"787-805\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert review of medical devices\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/17434440.2025.2520291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/6/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert review of medical devices","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17434440.2025.2520291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/6/17 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial intelligence and machine learning-driven design of self-healing biomedical composites.
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.