Special Issue Information
Bioprinting and biomedical additive manufacturing are rapidly evolving from “printable” structures to functional, intelligent, and application-ready systems, especially for implantable devices and patient-specific therapies. Progress in printable material systems—ranging from polymers and hydrogels/bioinks to bioceramics/bioactive glasses, metals/alloys, and hybrid composites—has enabled tailored architectures with controllable mechanical, biological, and physicochemical performance. At the same time, artificial intelligence (AI) is increasingly integrated across the workflow, supporting formulation design, parameter optimization, in situ monitoring, and quality assurance to improve reproducibility and translational potential. This Special Issue focuses on AI-enabled 3D printing for biomedical applications, with an emphasis on functional properties, material systems, and implant-oriented design and manufacturing. We welcome high-quality original research articles and reviews that connect materials–process–structure–function–biological response, and demonstrate clear biomedical relevance in vitro and/or in vivo. Material systems are not restricted. Contributions may address any printable platform, including polymers, ceramics, metals, and multi-material/hybrid systems. Implant-related studies are particularly encouraged, including work on long-term stability, interfacial biofunctionality, and performance in relevant physiological environments.
Scope and Topics
Topics of interest include (but are not limited to):
- 3D printing for biomedical implants: patient-specific implants, porous/lattice architectures, osseointegration strategies, surface/interface functionalization, fatigue and long-term reliability
- Material systems for implants and biodevices: metals/alloys, bioceramics/bioactive glasses, polymers, hydrogels/bioinks, and hybrid/multi-material composites;degradable vs. permanent systems
- Functional properties and performance: antibacterial/anti-inflammatory functions, osteogenic/angiogenic, controlled release, electrical/ionic functionality, smart/4D or stimulus-responsive behaviors (where relevant to biomedical use)
- AI in biomedical additive manufacturing: data-driven formulation design, printability and property prediction, parameter optimization (e.g., Bayesian optimization/active learning), interpretable ML, digital twins, and closed-loop control
- Process monitoring and quality assurance: in situ sensing/vision, defect detection, process drift correction, reproducibility and standardization for translational manufacturing