Safeguarding Generative AI Applications in Preclinical Imaging through Hybrid Anomaly Detection

TL;DR

This paper presents a hybrid anomaly detection framework to improve the safety and reliability of generative AI models in preclinical biomedical imaging, ensuring robustness and regulatory compliance.

Contribution

The paper introduces a novel hybrid anomaly detection approach specifically designed to safeguard generative AI applications in preclinical imaging systems.

Findings

Enhanced reliability of AI-generated images and data.

Reduced manual oversight through real-time outlier detection.

Improved robustness and scalability of AI models in biomedical applications.

Abstract

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.