Predicting the Future of Craniofacial Surgery: AI-Enabled Forecasting of Craniofacial Surgery Research and Future Clinical Priorities
Georgios Karamitros, Georgios Bouloukakis, Gregory A. Lamaris, Wesley P. Thayer, Galen Perdikis, Richard J. Redett, Robin Yang, Matthew Pontell, William C. LineaweaverBackground:
Artificial intelligence (AI) is increasingly used in clinical decision support and perioperative planning, yet its role in anticipating the future direction of surgical science remains underexplored. In craniofacial surgery, forecasting publication trajectories may help identify emerging clinical priorities, guide research investment, and support planning for future training, workforce, and patient-care needs. The objective was to develop and validate an AI-enabled, scenario-based framework for forecasting global craniofacial surgery research through 2030 across thematic, geographic, and economic strata.
Methods:
We performed a retrospective bibliometric study of PubMed-indexed craniofacial surgery publications from 15 peer-reviewed journals between 2010 and 2025. Automated extraction, semantic classification, and multimodel time-series forecasting were applied to generate conservative, moderate, and aggressive projections through 2030. Analyses were stratified by 6 thematic categories, 4 World Bank income groups, and 115 countries. Forecast uncertainty was reported using 95% prediction intervals.
Results:
A total of 25,625 publications were analyzed. Global output increased from 1059 publications in 2010 to 2688 in 2025, with a marked acceleration in the latter year. By 2030, projected global output ranged from 2235 to 4547 publications under the primary uncertainty interval, with conservative and aggressive scenario bounds of 2381 and 4278, respectively. Congenital and Pediatric research demonstrated the greatest absolute growth, whereas Aesthetic Surgery showed the fastest relative expansion. High-income countries accounted for 74.5% of total output, while lower-middle-and low-income countries contributed only 5.1% combined.
Conclusions:
AI-enabled forecasting converts publication trends into scenario-bounded foresight. By identifying where craniofacial research is accelerating, this framework may help journals, institutions, and training programs anticipate emerging clinical priorities, align resources with future patient-care needs, and support strategic planning for the evolving practice of craniofacial surgery.