AI in Fixed Prosthodontics: A Bibliometric and AI-Assisted Qualitative Analysis of Research Trends and Translational Gaps (2011–2026)

Background: A comprehensive understanding of the global research trends and translational gaps in AI for fixed prosthodontics remains limited. This study aimed to characterize research trends, delineate thematic clusters, and identify translational gaps in AI for fixed prosthodontics using an integrated bibliometric and AI-assisted qualitative synthesis framework. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection (2011–2026). A total of 519 records were retrieved and manually screened, yielding 377 eligible publications for quantitative analysis. Five highly cited core papers and 11 keyword clusters were further subjected to cross-model qualitative comparison using 4 large language models: GPT-5.2, Gemini-3 Pro, Claude-4.5 Sonnet, and DeepSeek Latest. Keyword co-occurrence, cluster, and temporal trend analyses were performed. The 4 AI systems were independently prompted to synthesize qualitative findings, and their outputs were compared for thematic convergence and divergence. Results: Keyword analysis revealed the top 3 terms as “artificial intelligence,” “deep learning,” and “dentistry,” indicating AI applications concentrate in the preoperative phase. Co-occurrence analysis identified clusters centered on deep learning, digital dentistry, 3D printing, and AI, suggesting convolutional neural networks and generative adversarial networks have extensively influenced prosthesis manufacturing. The 4 AI systems converged on deep learning as central but diverged in emphasis: GPT-5.2 produced the most integrated clinical narrative; Gemini-3 Pro maintained question-anchored thematic grouping; Claude-4.5 Sonnet applied evidence grading; and DeepSeek prioritized cluster-literature linkage to infer research gaps. Conclusions: Our findings indicate a rapidly accelerating, increasingly interdisciplinary domain transitioning from proof-of-concept toward clinical validation. While predictive and detection models predominate, generative AI demonstrates promising potential for crown design. Future progress should prioritize standardized benchmarking, robust external validation, and workflow-level translation into clinical decision-support. The integration of multiple AI models for qualitative synthesis offers a novel approach to identifying research trends and gaps.