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@ARTICLE{Reinke:303010,
author = {A. Reinke$^*$},
title = {{V}alidation of artificial intelligence algorithms for the
surgical practice. [{V}alidierung von künstliche
{I}ntelligenz-{A}lgorithmen für die chirurgische
{P}raxis.]},
journal = {Die Chirurgie},
volume = {96},
number = {11},
issn = {2731-6971},
address = {[Berlin]},
publisher = {Springer Medizin Verlag GmbH},
reportid = {DKFZ-2025-01457},
pages = {913-917},
year = {2025},
note = {#LA:E130# / 2025 Nov;96(11):913-917},
abstract = {Artificial 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.},
subtyp = {Review Article},
keywords = {Assessment methods (Other) / Metrics (Other) / Metrics
reloaded (Other) / Surgical video analysis (Other) / Video
data (Other)},
cin = {E130},
ddc = {610},
cid = {I:(DE-He78)E130-20160331},
pnm = {315 - Bildgebung und Radioonkologie (POF4-315)},
pid = {G:(DE-HGF)POF4-315},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:40643685},
doi = {10.1007/s00104-025-02348-2},
url = {https://inrepo02.dkfz.de/record/303010},
}