Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The intelligent lift: Artificial Intelligence's growing role in plastic surgery - a comprehensive review
6
Zitationen
1
Autoren
2025
Jahr
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.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.697 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.602 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.127 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.872 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.