Dipartimento di Ingegneria informatica, automatica e gestionale

The completion of human DNA sequencing in the early 2000s initially generated widespread excitement and hope that it would revolutionize medicine. Over time, however, it revealed major limitations due to a lack of understanding of the highly complex genotype-phenotype pathway. Precision medicine has emerged as a response to these biotechnological innovations, tailoring treatments based on an array of new molecular and clinical “omics” data. However, the large volume and heterogeneity of data available today requires the use of dedicated and highly efficient computational analyses. Widely used today are artificial intelligence techniques (such as machine learning) based on artificial neural networks, i.e., a mathematical model of how biological neurons work. Here, we show that artificial neural networks have nothing to do with biology, although their popularity is largely due to their alleged ability to simulate the human brain. Furthermore, we argue that the analysis of large molecular datasets cannot be left to the computational side alone, i.e., to be exclusively data-driven, but on the contrary must meet the challenge of integrating data and expertise, of getting clinicians and data analysts to work together to take into account the absolute and ineradicable uniqueness of each patient’s characteristics.