Personalized medicine is one of the main objectives of both basic and translational research within the larger paradigm of the so-called P4 medicine (Predictive, Preventive, Personalized, and Participatory). In order to achieve this goal and specifically to tailor therapeutic interventions to interindividual variability and the unique pathophysiological profiles of individual patients, synergistic and transdisciplinary data integration modelling and interpretation are indispensable. In the last three years, unique breakthroughs in the field of artificial intelligence (and deep learning in particular) have unlocked access to unprecedented data integration and prediction capabilities, allowing the seamless combination of information from, e.g., nuclear medicine, radiology, and anatomic pathology. On the data side, biomedical imaging in general and molecular imaging in particular can provide a foundation for the quantitative study of underlying mechanisms involved in human diseases and thus identify new promising molecular targets for both diagnosis and therapy. Therefore, the construction of a structured transdisciplinary collaboration and mutual enhancement model, both at a clinical and basic research level, can greatly accelerate the quest towards real, implementable personalized medicine strategies.

The focus of this special issue will be research dealing with potential new molecular targets for human disease therapy through the application of medical imaging techniques (e.g., radiological, molecular imaging, histopathology) and their integration using artificial intelligence. In particular, the special issue will be focused on in vitro, preclinical, and clinical investigations reporting findings aiming to advance the field of personalized medicine. Review articles focused on these topics are also welcome.

Potential topics include but are not limited to the following:

• Management of patients in the digital era: from basic science to personalized medicine
• Identification of new early prognostic/predictive biomarkers of oncological diseases
• New approaches for diagnosis and treatment of neurological disorders
• Emerging protocols for imaging of chronic inflammatory diseases
• Biomedical imaging data integration through artificial intelligence approaches
• Imaging mouse models of human diseases
• Deep learning for joint multiscale biomedical image analysis (e.g., from digital pathology to brain and body imaging)
• Artificial intelligence techniques for assisted diagnosis and prediction of longitudinal disease progression
• Analysis of clinical appropriateness through machine learning
• Prediction of clinical, neurophysiological, or molecular outcome measures through deep learning approaches
• Deep learning in nuclear medicine and molecular imaging
• Radiomics and deep learning in medical imaging