Generative AI holds great potentials to automate and enhance data synthesis in nuclear medicine. However, the high-stakes nature of biomedical imaging necessitates robust mechanisms to detect and manage unexpected or erroneous model behavior. We introduce development and implementation of a hybrid anomaly detection framework to safeguard GenAI models in BIOEMTECH's eyes(TM) systems. Two applications are demonstrated: Pose2Xray, which generates synthetic X-rays from photographic mouse images, and DosimetrEYE, which estimates 3D radiation dose maps from 2D SPECT/CT scans. In both cases, our outlier detection (OD) enhances reliability, reduces manual oversight, and supports real-time quality control. This approach strengthens the industrial viability of GenAI in preclinical settings by increasing robustness, scalability, and regulatory compliance.
@article{arxiv.2508.07923,
title = {Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection},
author = {Jakub Binda and Valentina Paneta and Vasileios Eleftheriadis and Hongkyou Chung and Panagiotis Papadimitroulas and Neo Christopher Chung},
journal= {arXiv preprint arXiv:2508.07923},
year = {2025}
}