Biomaterials can be defined as materials that interact positively with living tissues, restoring compromised functions, or enhancing tissue regeneration. Currently, biomaterial research often relies on a “trial-and-error method”, involving numerous experiments driven largely by experience. This strategy leads to a substantial waste of resources, such as manpower, time, materials, and finances. Optimizing the process is therefore essential. A recent and promising approach to this challenge involves artificial intelligence (AI), as demonstrated by the growing number of studies in this field. AI algorithms rely on data and empower computers with decision-making capabilities, mimicking aspects of the human mind and solving complex tasks with little to no human intervention. Due to their potential, AI and its derivatives are now widely used both in everyday life and in scientific research. In biomaterials science, AI models enable data analysis, pattern recognition, and property prediction. The aim of this review article is to highlight the key results achieved through the application of AI in the field of polymers for biomedical applications and, more broadly, in the development of advanced biomaterials. An overview will be provided on how an AI algorithm works, the differences between traditional programming and AI-based approaches, and their main limitations. Finally, the core topic will be addressed by categorizing biomaterials according to material class.
Martelli et al. (2025) studied this question.