Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

• Main Authors: Alfredo Cesario, Marika D’Oria, Francesco Bove, Giuseppe Privitera, Ivo Boškoski, Daniela Pedicino, Luca Boldrini, Carmen Erra, Claudia Loreti, Giovanna Liuzzo, Filippo Crea, Alessandro Armuzzi, Antonio Gasbarrini, Paolo Calabresi, Luca Padua, Guido Costamagna, Massimo Antonelli, Vincenzo Valentini, Charles Auffray, Giovanni Scambia
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Journal of Personalized Medicine
• Subjects: personalized medicine; systems medicine; gastroenterology; digestive endoscopy; cardiology; neurology
• Online Access: https://www.mdpi.com/2075-4426/11/4/265

Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in “Personalized Medicine” and “Innovative Biomedical Technologies”) could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.