A model for academic-industry partnerships to deliver clinical AI solutions that are fit for purpose
From Isabelle Hanlon
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.