Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation

Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation

Abstract We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-learning model for fetal growth scans usin...

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Bibliographic Details
Main Authors: Zahra Bashir, Manxi Lin, Aasa Feragen, Kamil Mikolaj, Caroline Taksøe-Vester, Anders Nymark Christensen, Morten B. S. Svendsen, Mette Hvilshøj Fabricius, Lisbeth Andreasen, Mads Nielsen, Martin Grønnebæk Tolsgaard
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Artificial intelligence, Fetal growth scans, Explainable AI, Human-AI collaboration
Online Access:https://doi.org/10.1038/s41598-025-86536-4
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