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000303010 1001_ $$0P:(DE-He78)97e904f47dab556a77c0149cd0002591$$aReinke, Annika$$b0$$eLast author$$udkfz
000303010 245__ $$aValidation of artificial intelligence algorithms for the surgical practice. [Validierung von künstliche Intelligenz-Algorithmen für die chirurgische Praxis.]
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000303010 520__ $$aArtificial intelligence (AI) is increasingly being used in surgery; however, the validation of such systems is often methodologically insufficient.Which validation issues arise in surgical AI and what requirements can be derived for clinically meaningful validation strategies?Metric-related pitfalls reported in the literature were analyzed, combined with insights from the interdisciplinary consensus process 'metrics reloaded' and its ongoing extension to surgical applications.Recurring weaknesses are observed at the levels of data, metrics and reporting. The lack of consideration of temporal structures and aggregation in video data is particularly critical.A structured, clinically grounded validation is essential for the safe use of surgical AI. The metrics reloaded procedure is currently being adapted to address surgery-specific requirements.
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000303010 650_7 $$2Other$$aAssessment methods
000303010 650_7 $$2Other$$aMetrics
000303010 650_7 $$2Other$$aMetrics reloaded
000303010 650_7 $$2Other$$aSurgical video analysis
000303010 650_7 $$2Other$$aVideo data
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