Agentic AI Meets Privacy-Preserving Infrastructure in Dentistry: A Systematic Review and the Dental AI Integration Framework (DAIF)

<b>Background:</b> Agentic AI systems and privacy-preserving data infrastructure have each advanced substantially in dentistry, yet no systematic review has examined their intersection or characterised the architectural gap between them.&nbsp; <div> <b><br></b> </div> <div> <b>Methods:</b> A PRISMA 2020-aligned systematic review searched PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library from inception to June 2026, supplemented by citation chaining. From 217 candidate papers, 127 were included and analysed across five standardised clinical dimensions: diagnostic accuracy, treatment personalisation, scheduling efficiency, reasoning traceability, and privacy and security robustness.&nbsp; </div> <div> <b><br></b> </div> <div> <b>Results:</b> Integrated multi-agent agentic AI frameworks consistently outperformed isolated models, with accuracy gains of up to 22.7% over state-of-the-art baselines. Dental imaging AI achieved pooled sensitivity 0.92 and specificity 0.94 for tooth detection and Dice Similarity Coefficient 0.93 for CBCT volumetric segmentation. Appointment scheduling was absent from most reviewed agentic AI papers despite direct patient safety implications for dental emergencies. Hallucination rates of 8-23% in medical large language models establish human-in-the-loop oversight as a clinical safety requirement. No identified paper integrated agentic clinical orchestration with blockchain governance and federated learning within a unified dental pipeline-a finding independently corroborated by a concurrent conference study.&nbsp; </div> <div> <b><br></b> </div> <div> <b>Conclusions:</b> A critical architectural gap exists at the intersection of agentic clinical intelligence and privacy-preserving data infrastructure in dentistry. Eight prioritised research gaps are identified, two rated Critical. The Dental AI Integration Framework (DAIF) is proposed as a four-layer evidence-based roadmap for responsible, production-grade clinical deployment. </div>