Ethical integration of artificial intelligence and additive manufacturing in dentistry

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.

Advanced AI techniques, including computer vision for in-process defect detection and reinforcement learning for workflow optimisation, can substantially reduce errors, material waste and the risk of clinical failure. However, these methods must be constrained by safety-first policies, continuous performance monitoring and explicit risk–benefit assessments that prioritise patient outcomes over purely operational gains. In dentistry, AI should be treated as an adjunct to—not a replacement of—professional judgement; dentists and technicians remain ethically and legally responsible for treatment decisions, regardless of how sophisticated AM and AI tools become.

Ongoing progress in AM technology and materials will increasingly be driven by AI, and both should evolve under well-defined responsible AI frameworks that emphasise fairness, reliability, safety, privacy, security, inclusiveness, transparency and accountability. Open communication among dentists, laboratory technicians, software and hardware developers, industrial partners, regulators and the media is essential to keep the community informed, identify emerging risks early and promote standards that ensure that AI-augmented AM serves patients ethically and constructively.