**2. Methodology**

A through literature search was conducted utilizing the online databases of PubMed, Google Scholar, ResearchGate as well as relevant websites. The search terms used were "artificial intelligence," "machine learning," "deep learning," "neural networks," "computer vision," "computer assisted surgery," "machine automation," "machine autonomy," "surgery automation," "surgery autonomy," "robotic surgery," "surgeon responsibility", "surgeon psychology", "surgical training," "technology adoption," and "levels of automation." Inclusion criteria were peer reviewed articles and book chapters published in the English language from 2018 to 2023. As this chapter includes a thorough examination of current technologies, product and company websites were also included that lead to further articles. Excluded articles included those that were not published in the English Language, that were not related to the subject and that were not available in full text.

Our search initially yielded 6887 articles. After excluding articles and removing duplicates, all abstracts were screened, resulting in 60 full text articles that met our inclusion criteria.

The snowball sampling technique was utilized to identify additional relevant articles by reviewing the reference lists of the included articles. This resulted in an additional 10 articles that met our inclusion criteria.
