Exploring Multifactorial Conflicts and Resolution Strategies in the Field of Peri-Implant Osseointegration: A Study Based on the WOE-NLP-Delphi Framework and Bibliometrics

Titanium implants remain the gold standard for dental and orthopedic rehabilitation, yet challenges in osseointegration and postoperative infection persist. This study integrated bibliometric analysis with an AI-driven framework (DeepSeek-R1) to map global research trends, quantify technical conflicts, and predict future translational trajectories. Analyzing 3078 publications (2015–2025) from Web of Science, the authors identified China as the most productive country (251 articles), while Switzerland led in citation impact (25 citations/article). Key institutions included Sichuan University (251 publications) and Seoul National University (183 publications). Methodologically, the authors introduced a Weight of Evidence-Natural Language Processing-Delphi (WOE-NLP-Delphi) model to quantify core technical conflicts, revealing critical trade-offs: anti-inflammatory versus angiogenesis (conflict intensity=3.5), surface roughness versus inflammation (3.0), and antibacterial versus osteogenic activity (2.5). Technology Readiness Level (TRL) coupled with quantum Monte Carlo simulations predicted titanium surface topooptimization (TRL=6.8, R ²=0.89) as clinically translatable, while flagging risks in sulfur-doped PEEK/Zn composites. Our findings highlight 3D-printed antimicrobial peptide coatings as high-potential candidates. This study establishes a paradigm for resolving multifactorial conflicts in implant engineering, offering data-driven insights to accelerate clinical translation.