[Validation of artificial intelligence algorithms for the surgical practice].

BACKGROUND

Artificial intelligence (AI) is increasingly being used in surgery; however, the validation of such systems is often methodologically insufficient.

OBJECTIVE

Which validation issues arise in surgical AI and what requirements can be derived for clinically meaningful validation strategies?

METHODS

Metric-related pitfalls reported in the literature were analyzed, combined with insights from the interdisciplinary consensus process "metrics reloaded" and its ongoing extension to surgical applications.

RESULTS

Recurring weaknesses are observed at the levels of data, metrics and reporting. The lack of consideration of temporal structures and aggregation in video data is particularly critical.

DISCUSSION

A structured, clinically grounded validation is essential for the safe use of surgical AI. The metrics reloaded procedure is currently being adapted to address surgery-specific requirements.