The Divison of Nuclear Medicine in collaboration with the QIMP group (Univ. Prof. Thomas Beyer, Center for Medical Physics and Biomedical Engineering) has been engaged in artificial intelligence/machine learning projects since 2016, primarily focusing on tumour characterization. These activities specificially focus on quantitative radiomics  and machine learning  as well as holomics (holistic-omics)  which combines PET, PET/CT and PET/MRI imaging and non-imaging data to more accurately establish perdictive models, pointing towards precision medicine.
As of January 2019, the Divison of Nuclear Medicine plays a prominent role as part of the Applied Metabolomics Christian Doppler Laboratory (CDL), where hybrid imaging, liquid biopsy, clinical as well as patient demographics data combined together are analyzed with state-of-the-art radiomic and deep learning approaches.
1. Papp L, Rausch I, Grahovac M, Hacker M, Beyer T. Optimized feature extraction for radiomics analysis of 18 F-FDG-PET imaging. J Nucl Med. November 2018:jnumed.118.217612.
2. Papp L, Poetsch N, Grahovac M, et al. Glioma survival prediction with the combined analysis of in vivo 11C-MET-PET, ex vivo and patient features by supervised machine learning. J Nucl Med. 2017;59:jnumed.117.202267.
3. Papp L, Spielvogel CP, Rausch I, Hacker M, Beyer T. Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis. Front Phys. 2018;6.