TY - JOUR A1 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D ER -