Enhancing Biomedical Metal 3D Printing with AI and Nanomaterials Integration

The integration of artificial intelligence (AI) with nanomaterials is rapidly transforming metal three-dimensional (3D) printing for biomedical applications due to their unprecedented precision, customization, and functionality. This article discusses the role of AI in optimizing design parameters, predicting material behaviors, and controlling additive manufacturing processes for metal-based implants and prosthetics. Nanomaterials, particularly metallic nanoparticles, enhance the mechanical strength, biocompatibility, and functional properties of 3D-printed structures. AI-driven models, including machine learning (ML) and deep learning algorithms, are increasingly used to forecast print quality, detect defects in real-time, and reduce material waste. Moreover, data-driven design approaches enable patient-specific implant development and predictive modeling of biological responses. We highlight recent advancements in AI-guided material discovery through microstructure–property correlations and multi-scale simulation. Challenges such as data scarcity, standardization, and integration across interdisciplinary domains are also discussed, along with emerging solutions based on federated learning and the digital twinning approach. Further, the article emphasizes the importance of AI and nanomaterials to revolutionize metal 3D printing to fabricate smarter, safer, and effective biomedical devices. Future perspectives covering the need for robust datasets, explainable AI frameworks, and regulatory frameworks to ensure the clinical translation of AI-enhanced additive manufacturing technologies are discussed.