Artificial Intelligence in Biomedical 3D Printing: Mapping the Evidence

Abstract

This study provides an integrated synthesis of Artificial Intelligence (AI) applications in Biomedical 3D Printing, mapping the conceptual and structural evolution of this rapidly emerging field. The bibliometric analysis, based on 229 publications indexed in the Web of Science Core Collection (2018–2025) and visualised in CiteSpace, identifies three interconnected research domains: AI-driven design and process optimisation, data-assisted bioprinting for tissue engineering, and the development of smart and adaptive materials enabling 4D functionalities. The results highlight a clear progression from algorithmic control of additive manufacturing parameters toward predictive modelling, deep learning, and autonomous fabrication systems. Leading contributors include China, India, and the USA, while journals such as Applied Sciences, Polymers, and Advanced Materials act as major dissemination platforms. Emerging clusters around “4D printing”, “deep learning”, and “shape memory polymers” indicate a shift toward intelligent, sustainable, and personalised biomanufacturing. In addition, a qualitative synthesis of the most influential papers complements the bibliometric mapping, providing interpretative depth on the scientific core driving this interdisciplinary evolution. Overall, the study reveals the consolidation of a multidisciplinary research ecosystem in which computational intelligence and biomedical engineering converge to advance the next generation of adaptive medical fabrication technologies.