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Checcucci et al. Mini-invasive Surg 2021;5:49 https://dx.doi.org/10.20517/2574-1225.2021.98 Page 3 of 4

Theoretically, to reach this quality of information, multiple tertiary centers should provide standardized
data, following uniform standards. Deep learning models developed from these data may be able to predict
unexpected complications, offering the surgeon a chance to adjust the intraoperative planning. The robotic
system would also be able to recognize the operator and adapt its feedback to the surgeon, providing
instantly tailored data to reach the best and smartest surgical decision making . Moreover, exploiting the
[16]
available cloud services and high-speed internet connection (i.e., 5G), information can be rapidly exchanged
between machines.

Even if this scenario sounds appealing, the assurance of data secrecy and the lack of precise legislation
represent technical obstacles which still need to be overcome.

In conclusion, particularly in an intraoperative setting, the advent of AI is obstacle by the lack of live data
collection and by the complexity of privacy and data sharing legislation.

For all these reasons, the current research should be focused on the ability of AI to provide the operator
important additional information (e.g., augmented reality images) during the surgical procedure, rather
than trying to substitute the surgeon.

DECLARATIONS
Acknowledgement
We would like to thank Dott. Paolo Verri for his support in this study

Authors’ contributions
Study concept and manuscript writing: Checcucci E, Porpiglia F

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.

Conflicts of interest
Both authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2021.

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