Page 105 - Read Online

Page 432 Roy et al. Art Int Surg 2024;4:427-34 https://dx.doi.org/10.20517/ais.2024.69

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.

REFERENCES
1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56. DOI PubMed
2. Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting):
developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc 2020;27:2011-5. DOI PubMed PMC
3. Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic errors? N Engl J Med 2010;362:1066-9. DOI