AI-enhanced design tools can simplify manufacturing, rationalise material use and accelerate production, but they must be evaluated for accuracy, repeatability, safety and performance across complex patient populations and clinical variables. Early integration of AI into the design and planning stages amplifies its impact, making it critical to embed ethics-by-design measures: representative training data, clear documentation of capabilities and limitations, and mechanisms for clinicians to review and override automated suggestions.
Conventional AM quality control relies on validated process parameters and post-production inspection, whereas AI-driven quality control is designed to detect deviations during fabrication and enable earlier intervention, reducing waste and inefficiency. However, clinicians and technicians must understand the known limitations of these systems, including false-positive and false-negative findings, missed defects and inappropriate design suggestions. They must also remain alert to new kinds of errors introduced by AI-assisted workflows and avoid over-reliance on opaque AI outputs. When AI systems pre-analyse data and workflows to remove bottlenecks, any patient-related or commercially sensitive data must be subject to strict requirements for privacy, consent and security, and responsibility for outcomes must remain clearly assigned to identifiable human professionals.
AM in dentistry already spans resins, resin composites, ceramics, metals and emerging polymer–metal blends, but each new combination raises ethical questions about long-term safety, traceability and accountability particularly when AI-assisted design affects the fit, strength, safety or clinical performance of a restoration or device. Bioprinting with hydrogels and bioinks for tissue scaffolds and related medical applications represents a particularly sensitive frontier, where AI-optimised AM must be coupled with rigorous ethical review, regulatory oversight and robust informed consent processes. AI’s role in shaping the design rationale for these materials also creates a duty to document the relevant tools, assumptions, software versions and validation evidence so that AI-influenced design decisions can be traced and reviewed throughout the lifespan of the restoration or device.
