A narrative review of AI-driven predictive maintenance in medical 3D printing

The integration of Artificial Intelligence (AI)-driven predictive maintenance (PM) into the management of medical 3D printing machines promises a revolutionary advancement in operational efficiency and reliability. Traditional maintenance approaches often fail to optimize machine uptime and reduce costs effectively. AI and Machine Learning (ML) techniques, however, enable a proactive maintenance strategy by predicting potential failures and facilitating timely interventions. This ensures continuous high-quality production, which is critical for patient safety and effective medical care. AI-driven PM employs various methods, including supervised and unsupervised learning, neural networks, and natural language processing, to analyze vast datasets and perform intelligent diagnostics. The incorporation of advanced technologies such as digital twins, generative AI, collaborative robots, blockchain, and the Industrial Internet of Things (IIoT) further enhances fault detection, transparency, and efficiency in maintenance processes. The benefits of this approach include minimized downtime, cost savings, improved safety, and increased equipment reliability. As AI technology advances, its role in PM will become more sophisticated, establishing new benchmarks for the operation and management of 3D printing machines. This proactive maintenance strategy not only improves performance but also provides a competitive advantage in the healthcare sector.