Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Next-Gen Surgery: AI’s Transformative Impact on Oral and Maxillofacial Surgery
0
Zitationen
1
Autoren
2026
Jahr
Abstract
The advent of Artificial Intelligence (AI) is revolutionising the landscape of oral and maxillofacial surgery (OMFS), enhancing diagnostic capabilities, surgical planning, and postoperative care. AI technologies, includingmachine learning and deep learning, offer valuable insights that support surgeons in making informed decisions and improving patient outcomes. By analysing complex medical data, AI assists in assessing imagesbefore and after surgical procedures, which helps to reduce patient dissatisfaction, particularly in aesthetic surgeries. This review article aims to explore AI’s transformative potential in OMFS, evaluating recent technological advancements, identifying current challenges, and discussing future prospects. The successful integration of AI within OMFS relies on a synergistic relationship between advanced technology and the nuanced expertise of skilled surgeons, who are essential for high-quality patient care. Continued research is crucial to refine AI algorithms, ensuring they complement rather than replace the critical role of human practitioners in optimising therapeutic outcomes. How to cite this article:Ahamed A S, Mubeena, Kumar N D. Next-GenSurgery: AI’s Transformative Impact on Oral andMaxillofacial Surgery. Chettinad Health City MedJ. 2025;14(4):54-59. DOI: https://doi.org/10.24321/2278.2044.202549
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.423 Zit.