[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.