The intelligent lift: Artificial Intelligence's growing role in plastic surgery - a comprehensive review
Bibliographic record
Abstract
Background: Artificial Intelligence (AI) is rapidly transforming plastic surgery by enhancing diagnostic precision, surgical planning, and postoperative evaluation. Despite promising results in algorithmic performance, the clinical utility and ethical implications of AI in this specialty remain underexplored. Methods: This study systematically reviewed literature from January 2010 to May 2025 across PubMed, Scopus, Web of Science, and IEEE Xplore. Included studies evaluated AI applications in plastic surgery using validated models and reported performance metrics. Quality assessment was performed using QUADAS-2, Newcastle-Ottawa Scale, and TRIPOD-AI criteria. A random-effects meta-analysis summarized pooled accuracy across domains. Results: = 32%). Postoperative evaluation showed the highest accuracy (90%), followed by preoperative planning (88%) and predictive modeling (86%). Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) demonstrated strong performance in image-based and predictive tasks, respectively. However, fewer than 40% of studies reported external validation, and none included prospective clinical trials. Ethical concerns, limited data diversity, and methodological inconsistencies were prevalent. Conclusion: This study confirms AI's significant potential in plastic surgery for enhancing surgical precision and personalized care. However, clinical integration is hindered by inadequate validation, transparency, and demographic representation. Advancing the field requires standardized protocols, multicenter collaborations, and ethical frameworks to ensure safe and equitable deployment of AI technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".