Dr. Cesare    Furlanello
Fondazione Bruno Kessler (FBK) Trento

Almost every clinical specialty will use Artificial Intelligence (AI) in the future;
however, pre-clinical testing of medical AI systems does not ensure yet to
reach the goals of precision medicine. This talk will discuss how to bridge the
gap between top-notch deep learning research and effective clinical impact,
dealing with reproducibility, accuracy, interpretability. Methods to identify
disease trajectories from integrative data, will be presented, coping with
selection bias and hidden stratification by means of valid Data Analysis Plans,
and showcasing studies on Deep Learning applied to genomics and digital
pathology.

Cesare Furlanello is a data scientist, head of the FBK Unit for Predictive
Models on Biomedical and Environmental Data. He is an expert in machine
learning and bioinformatics applied to multi-modal health data with Deep
Learning. Bioinformatics collaborator of US FDA/NCTR, RIKEN, and adjoint
Faculty of the Wistar Institute in Philadelphia, he is the Past President of the
MAQC Society for reproducibility and quality control over massive data.