When AI and Big Data meet Life Sciences : advances in research and ethical questions

Nowadays Big Data and the subsequent Artificial Intelligence (AI) are everywhere, including in Life Science Research. As young researchers we are collecting more and more data every day, and big data and AI can be powerful tools in our research, even more so in ‘omics’ or predictive medicine. However, using such tools raises several ethical questions, especially regarding patient data safety.

How long can we keep data? How do we safely store data, and protect it from hackers? Can we analyse data without asking patients? Should we predict diseases at early stages when there is no therapeutic solution?  In this roundtable, experts from different fields will share their opinion with the public on this subject.

Isabelle Ryl

Isabelle Ryl holds a doctorate in Computer Science from the University of Lille (1998) and an habilitation to direct research (2006). After her PhD, she completed a post-doctorate at the University of Oslo before joining the University of Lille in 1999 as a professor. From September 2010 to March 2018, she was director of the Paris Inria research center, whose project-teams work in many areas related to artificial intelligence. She has been a member of numerous academic boards such as Paris Sciences et Lettres and Sorbonne Universités. From March 2018 to September 2018 she was acting Deputy CEO for Transfer and Industry Partnerships at Inria. She has been vice-president of Cap Digital (french excellence cluster for digital content and services) since 2014 and a member of the French Innovation Council since July 2018. Since October 2018, she has been in charge of a new project, the creation of an AI institute in Paris (PRAIRIE), which is a candidate to become one of the 3IA Institutes of the French national AI initiative.

Xosé Fernández

Dr Xosé M Fernández is a computational biologist with over 20 years’ experience in the UK and France. Currently, he’s the Chief Data Officer at Curie Institute where he leads the design and implementation of the institutional data strategy, bringing a current knowledge and future vision of emerging technologies, including mHealth, AI and digital technologies. With a clear mission: managing the digital transformation by implementing changes in organisation, governance, capabilities, business processes, and culture, as we navigate the intricacies of both legacy IT architectures and fresh demands from novel digital infrastructures. He aims to use data science, deep learning, computational genomics, biomedical informatics and translational bioinformatics approaches to enable research discoveries and practical innovations in oncology.

Marie Pauline Talabard