Abstract Background Artificial intelligence (AI) has transformed clinical decision making, yet its application to forecasting the evolution of surgical science remains underdeveloped. Anticipating future research trajectories represents a critical unmet need for strategic planning, workforce allocation, and innovation stewardship in aesthetic surgery. Objectives The aim of this study was to develop and validate an AI-assisted forecasting framework capable of modeling and predicting global aesthetic surgery research activity. Methods We performed a population-level observational analysis of all PubMed-indexed aesthetic surgery publications from 2010 to 2024. A fully autonomous AI pipeline conducted large-scale data ingestion, followed by high-fidelity semantic classification of publications by research domain and country (validated accuracy 97%). Annualized outputs were analyzed using optimized exponential-smoothing and autoregressive time-series models to generate long-horizon forecasts with 95% CIs. Results The framework processed 24,026 records, yielding 23,521 eligible publications across 13 journals. Exponential smoothing demonstrated superior predictive performance (R2 = 0.94, root mean square error = 166.6). Global research output is projected to increase by 21.9% by 2030, reaching 2939 publications annually (95% CI, 2612-3265). Minimally invasive and injectable research exhibited the steepest projected growth (+46.1 publications/year). Conclusions This study establishes AI-driven forecasting as a next-generation analytic paradigm for surgical meta-research. By integrating autonomous data ingestion, semantic intelligence, and rigorously validated time-series modeling, the framework operationalizes predictive intelligence—shifting aesthetic surgery research from retrospective surveillance to prospective trajectory mapping. The resulting system is scalable, reproducible, and continuously recalibratable, positioning AI as a strategic instrument for anticipatory research governance, resource allocation, and human-capital planning in surgical science. Level of Evidence: 5 (Therapeutic)
Karamitros et al. (Tue,) studied this question.