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Roy et al. Art Int Surg 2024;4:427-34 Artificial
DOI: 10.20517/ais.2024.69
Intelligence Surgery

Commentary Open Access

Clinical deployment of machine learning models in
craniofacial surgery: considerations for adoption

and implementation

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4,5
2
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Mélissa Roy 1 , Russell R. Reid , Senthujan Senkaiahliyan , Andrea S. Doria , John H. Phillips , Michael
Brudno 6,7,8,9 , Devin Singh 10
1
Division of Plastic, Department of Surgery, McMaster University, Hamilton L8S 4L8, Canada.
2
Section of Plastic Surgery, Department of Surgery, University of Chicago, Chicago, IL 60637, USA.
3
Division of Plastic, Reconstructive and Aesthetic Surgery, The Hospital for Sick Children, Toronto M5G 1X8, Canada.
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Department of Medical Imaging, University of Toronto, Toronto M5T 1W7, Canada.
5
Department of Diagnostic Imaging, Research Institute, The Hospital for Sick Children, Toronto M5G 1E8, Canada.
6
Department of Computer Science, University of Toronto, Toronto M5S 2E4, Canada.
7
Vector Institute, Toronto M5G 1M1, Canada.
8
Digital Team and Techna Institute, University Health Network, Toronto M5G 2C4, Canada.
9
Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto M5G 0A4, Canada.
10
Department of Paediatrics, The Hospital for Sick Children, Toronto M5G 1X8, Canada.
Correspondence to: Dr. Devin Singh, Department of Paediatrics, The Hospital for Sick Children, 555 University Avenue, Toronto
M5G 1X8, Canada. E-mail: devin.singh@sickkids.ca
How to cite this article: Roy M, Reid RR, Senkaiahliyan S, Doria AS, Phillips JH, Brudno M, Singh D. Clinical deployment of
machine learning models in craniofacial surgery: considerations for adoption and implementation. Art Int Surg 2024;4:427-34.
https://dx.doi.org/10.20517/ais.2024.69
Received: 16 Aug 2024 First Decision: 24 Sep 2024 Revised: 11 Nov 2024 Accepted: 14 Nov 2024 Published: 13 Dec 2024
Academic Editors: Ernest S. Chiu, Andrew A. Gumbs Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang
Abstract
The volume and complexity of clinical data are growing rapidly. The potential for artificial intelligence (AI) and
machine learning (ML) to significantly impact plastic and craniofacial surgery is immense. This manuscript reviews
the overall landscape of AI in craniofacial surgery, highlighting the scarcity of prospective and clinically translated
models. It examines the numerous clinical promises and challenges associated with AI, such as the lack of robust
legislation and structured frameworks for its integration into medicine. Clinical translation considerations are
discussed, including the importance of ensuring clinical utility for real-world use. Finally, this commentary brings
forward how clinicians can build trust and sustainability toward model-driven clinical care.

Keywords: Artificial intelligence, machine learning, craniofacial surgery, clinical translation
CLINICAL DEPLOYMENT OF MACHINE LEARNING MODELS IN CRANIOFACIAL

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
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