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               maintenance of AI algorithms, such as data scientists and machine learning operations (MLOps) engineers.
               For example, MLOps engineers will help create systems that continuously monitor models to ensure a
               decline in model performance does not occur without clinical and operational teams being aware. This is
               critical as models can decline in performance based on data drift and other changes in the clinical real-
               world environment. To support this, business models and associated governance structures should be
                     [43]
               created . They may vary in size and range from innovation clusters, combining local expertise in AI,
               translational research, digital health, statistics, and more, to centers of excellence in large organizations .
                                                                                                     [44]
               Final thoughts
               As we enter an age of increased intersections between society, data, and technology, we will notice the rapid
               proliferation of ML models migrating from development labs into real-world surgical settings. We
               recommend craniofacial surgeons be open and enthusiastic about upcoming ML models and tools but also
               aware of their numerous limitations. Clinical deployment of such models is arduous yet promising for the
               advancement of surgical care at the patient, team and system levels. Successful and safe integration of these
               models into practice requires input from surgeons. Although clinical expertise cannot be replaced at this
               time, it can be augmented by ML models, and surgeons should not be afraid of being innovators or early
               adopters if they are equipped with knowledge and awareness.

               The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge - Stephen Hawking


               DECLARATIONS
               Authors’ contributions
               Made substantial contributions to the conception, literature search, writing and review of the study: Roy M,
               Reid RR, Senkaiahliyan S, Doria AS, Phillips JH, Brudno M, Singh D

               Availability of data and materials
               Not applicable.

               Financial support and sponsorship
               None.

               Conflicts of interest
               Singh D is the co-founder and CEO of a Canadian healthcare technology start-up company called Hero AI,
               while the other authors have declared that they have no conflicts of interest.


               Ethical approval and consent to participate
               Not applicable.


               Consent for publication
               Not applicable.


               Copyright
               © The Author(s) 2024.

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