# Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection

arXiv: https://arxiv.org/abs/2508.07923 · HTML: https://mlsanity.com/paper/2508.07923/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Out-of-distribution Detection in Medical Image Analysis: A survey](https://mlsanity.com/paper/2404.18279/) (2024)
- [Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey](https://mlsanity.com/paper/2505.02448/) (2025)

## Built on by (most cited first, 1 of 1)

- [Explain and Monitor Deep Learning Models for Computer Vision using Obz AI](https://mlsanity.com/paper/2508.18188/) (2025)

## Abstract (arXiv)

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.