Magnetic Nanoparticles (MNPs) are unique complex objects whose physical properties differ greatly from their parent massive materials. In fact, the magnetic properties are particularly sensitive to the particle size, being determined by finite size effects on the core properties, related to the reduced number of spins cooperatively linked within the particle, and by surface effects, becoming more important as the particle size decreases.

MNPs have generated much interest because of their possible applications in high density data storage, ferrofluid technology, catalysis, environmental technology, and biomedicine (e.g., drug delivery, contrast enhanced MRI). To synthetize Magnetic nanoarchtiecture (MN) represent an additional tool to further tuning physical properties of MNPs, obtaining new multifunctional materials. MN consist in a magnetic core embedded in shell/matrix that may be composed of polymers, mesoporous structures (e.g., silica, zirconia, zeolites, metalorganic framework) or even molecules.

Shell/matrix can have magnetic properties and in this case properties of MN rely even more strong on the interplay between those of the constituent components. When the individual components themselves, are complex systems belonging for examples to the family of correlated electron oxide with exotic physical properties, it becomes non-trivial and extremely fascinating to customize the properties of these bi-magnetic nanocomposites.

Based on this framework, this talk will focus on the design of MN that means to control the matter at the nanoscale, correlating magnetic properties, micro- and meso-structure and molecular coating. Some recent results on synthesis of magnetic nanocomposites and their application in energy (e.g., permanent magnets, thermoelectricity), biomedicine, catalysis and other technological field will be discussed.

Artificial Intelligence (AI) has been implemented in the field of Medical Imaging since the eighties. The great emphasis in recent years on Deep Learning methods is triggering a new revolution in this field, potentially leading to improve disease diagnosis and patient prognosis.

Some specific challenges related to methodological aspects need to be addressed before the AI potential can be fully exploited in Medical Imaging applications. While AI models require large amounts of labelled data during the training phase, it is very difficult to collect sufficiently large samples of properly annotated medical images. Thus, efficient and robust strategies to learn from limited examples have to be set up.

An additional crucial challenge to face in medical applications of AI is to make these solutions understandable by humans. Dedicated Explainable AI (XAI) solutions are urgently needed in the field of medical applications to make clinicians and patients trust AI-based solutions and to enable their adoption in clinical workflows.