The Artificial Intelligence and Data Science Laboratory (LIAD) at CEA-ISAS specializes in statistical learning and probabilistic modeling of uncertainties in numerical simulations. It focuses on developing machine learning methods and their application to physical problems, particularly in gamma-ray detection, with a strong emphasis on the reliability of results. They are involved in the project as coordinator and in the development of Machine Learning algorithms for processing signals from the detector.

The Groupe d’Instrumentation pour la Santé (GIS) collaborates closely with CEA-IRFU/DEDIP and CNRS/IJC-Lab on detector development. The group is actively involved in developing the ClearMind detector, testing detector modules on specialized benches, and creating simulations to generate training data. Their expertise in detector physics, data preprocessing, and signal interpretation is crucial for the AI detector development, ensuring the production of high-quality data and the development of robust algorithms. 

The BioMaps unit specializes in biomedical imaging, including PET, and develops Monte Carlo simulation and tomographic reconstruction methods for PET images. Its involvement is crucial for the AI imaging approaches, particularly for producing realistic simulation data for studying and validating methods, as well as for developing AI methods for PET imaging. Additionally, the BioMaps UMR is associated with the Frédéric Joliot Hospital Service, and its expertise is necessary to assess the benefits of the AI approach for patients 

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The OPIS team at Inria Saclay develops advanced methods for analyzing and processing large and complex data based on non-smooth optimization, fixed point theory, parallel/distributed strategies, Bayesian inference, and neural networks. Their expertise is central to the AI imaging in developing robust, explainable, and trustworthy methods for image reconstruction, particularly through unfolding or Plug-and-Play approaches.​

The imXgam research team of CPPM develops X-ray and gamma-ray imaging techniques. It has created the tomXgam mechanical tomographic experimentation bench, which is used to produce experimental data for both calibrating simulations and testing the entire detection chain, from data acquisition to image reconstruction algorithms. The laboratory also contributes to the Monte Carlo modeling of the ClearMind detectors.