Abstract: A correct assessment of
patients’ diagnostic, prognostic, and therapy response to standard of care
treatments, must consider individual variabilities, including prediction of
adverse therapy side effects, and likelihood of surge of co-morbidities. The
complexity of the anamnestic, imaging, laboratory, pathology, omics data
available for each patient is a major obstacle to reach this goal in the
day-by-day clinical practice.
The development and validation of
safe, evidence-based Artificial Intelligence platforms is a possible solution
to this need. The precise clinical definition of the most relevant data,
obtained from different sources, and of the outcomes of interest, as well as
the knowledge of the complexity of patients’, doctors’ and data journey within
the hospital, play a pivotal role to create comprehensive data lakes and to
establish standard trustworthy operating procedures of real practical
usefulness to the scientist and to the clinician, and easily deployable both in
large multi-specialistic hospitals, and in smaller community hospitals.
In this presentation I will describe two use
cases (Covid-19 and lung cancer) as blueprint of the successful strategic
partnership between San Raffaele Hospital and Microsoft that led to the design
of a cloud-based AI multilayered platform. In addition, I will touch upon the
advantages of adopting a Federated Machine Learning model to allow
collaborations between different hospitals providing trustworthy GPDR compliant
tools for Machine Learning model exchange, avoiding export of data from one
institution to another.