AI-assisted forecasting in microsurgery: A dual-component framework for global publication trends

Background: Artificial intelligence (AI)-assisted approaches may allow surgical research trends to be analyzed at scale and projected over time. However, their use in forecasting the evolution of microsurgical scholarship remains limited. This study developed an AI-assisted bibliometric framework to characterize and project global clinical and experimental microsurgery publication trends. Methods: PubMed metadata from 20 microsurgery-relevant surgical journals were extracted for 2010–2024 using an automated Python-based retrieval algorithm. A rule-based contextual key-word classifier using a predefined microsurgery keyword taxonomy was applied to identify relevant publications. Candidate forecasting models included linear regression, quadratic regression, autoregressive integrated moving average, and Holt’s exponential smoothing. Model performance was compared using R2, root mean square error, mean absolute error, and Akaike information criterion. Forecasts were generated through 2030 and reported with 95% confidence intervals. Results: The framework processed 90,902 records, of which 83,133 underwent contextual text classification after exclusion of incomplete metadata. A final analytic dataset of 11,561 microsurgery publications with verifiable first-author country attribution was identified. Classification validation using a stratified sample of 4,441 records demonstrated 95.2% agreement with the human-reviewed reference standard (95% CI, 94.5%–95.8%; Cohen’s κ = 0.826). Annual microsurgery publications increased from 611 in 2010–973 in 2024, representing a 59.3% increase. Temporal validation supported short-horizon stability of the linear projection model. In fixed temporal holdout testing, the linear model achieved RMSE 40.5, MAE 36.2, MAPE 4.0%, and 95% prediction-interval coverage of 100%. Through 2030, publication activity is projected to increase, with lymphatic microsurgery showing the greatest relative thematic growth (+36.0%), followed by technological and operative innovation (+21.7%). Conclusion: This study presents an AI-assisted bibliometric workflow for characterizing and projecting microsurgical publication trends. By integrating automated PubMed metadata extraction, contextual keyword-based classification, human-reviewed validation, and conventional statistical forecasting, the workflow enables reproducible assessment of publication activity across time, geography, authorship, and thematic domains. The resulting estimates should be interpreted as conditional projections under observed historical trends rather than deterministic predictions of future scientific activity, innovation, or leadership. This approach may assist surgeons, clinician-scientists, and interdisciplinary research teams in summarizing research patterns, identifying areas of increasing scholarly activity, and informing future collaborative planning.