AI methodologies have been applied to medical research for years, and has recently made an impactful entrance in oncology more specifically. AI broadly speaking consists in a set of techniques allowing computers to emulate human intelligence, employing algorithms created for the analyses and the design of either predictions or conclusions based on the analysis of big datasets.
The latter is especially important for cancer research considering the critical mass of data available for analysis and that standard analysis methods fail to exploit to its fullest potential. This is particularly the case for multiomics data, with their high variation in nature, format or storage.
The proper and effective and integration of these novel methodologies into the standards of clinical – but also basic and translational – research could prove to be an important leap forward for oncology research. Hence, this event will have two core training objectives.
The first will be to ease the clinical and research community into the mindset of AI methodologies themselves, as it has been applied to the medical field for years now, for instance in designing medical devices, yet is still misunderstood or not known to its full potential – from a general overview of the most frequently used ML/DL methods and Explainable AI to a deep dive in novel data platforms and repository structures integrating these approaches in their design. This will allow clinicians to identify the value of AI models for their trials and studies, making the volume of patient- and tumor-related data valuable and more fully exploitable; as well as biologists to increase the playing field in tumor biology to discover new biomarkers and mechanisms.
The second main endpoint will be to demonstrate not only the possibilities offered by the inclusion of AI models in standard practice, but really to present some concrete and innovative activities where they are already being successfully implemented. The focus is to demonstrate in particular the value of AI for both its predictive power and for the possibilities it opens up for the discovery of both new biomarkers and of new molecules targeting specific tumors. In particular, one section will be focused on the translational field and the synergy between AI-powered multiomic data analysis and clinical research, with regards to cancer immunotherapy.
As the AI research field is evolving at a rapid pace, the event will be topped off by a session offering perspectives already going beyond the current state of the art and providing insights into the AI of tomorrow – how it could be involved as full-fledged actor in clinical decision-making, all the way to the field of quantum sciences.
SCIENTIFIC COMMITTEE:
Arsela Prelaj, MD, PhD student
Medical Oncologist and PhD student in Bioengineering and Artificial Intelligence
Polytechnic University of Milan
Thoracic Oncology Unit, Medical Oncology Department 1
Fondazione IRCCS Istituto Nazionale Tumori, Milan (Italy)
HOW TO PARTECIPATE/ECM:
The conference will be held in a hybrid mode, with participants attending both in presence and online.
Both modalities, include the ECM programme (RES for presence, FAD SICRONA for online). To register, please use the form with regard to the selected participation method. It is possible to attend only one modality.
