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Review
. 2021 Apr 2;11(4):265.
doi: 10.3390/jpm11040265.

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Alfredo Cesario  1 , Marika D'Oria  1 , Francesco Bove  2   3 , Giuseppe Privitera  4   5 , Ivo Boškoski  6 , Daniela Pedicino  7 , Luca Boldrini  8 , Carmen Erra  9 , Claudia Loreti  9 , Giovanna Liuzzo  7 , Filippo Crea  7 , Alessandro Armuzzi  4   5 , Antonio Gasbarrini  4   5 , Paolo Calabresi  2   3 , Luca Padua  9 , Guido Costamagna  6 , Massimo Antonelli  10 , Vincenzo Valentini  8 , Charles Auffray  11 , Giovanni Scambia  12   13
Affiliations
Review

Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence

Alfredo Cesario et al. J Pers Med. .

Abstract

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.

Keywords: P4 medicine; artificial intelligence; cardiology; digestive endoscopy; gastroenterology; machine learning; neurology; neurorehabilitation; personalized medicine; systems medicine.

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Conflict of interest statement

I.B. is Consultant for Apollo Endosurgery, Cook Medical, and Boston Scientific; board member for Endo Tools; research grant recipient from Apollo Endosurgery. G.C. is Consultant for Cook Medical, Boston Scientific, and Olympus. G.P. received consultancy fees from Alphasigma and speaker fees from Janssen. A.G. reports personal fees for consultancy for Eisai S.r.l., 3PSolutions, Real Time Meeting, Fondazione Istituto Danone, Sinergie S.r.l. Board MRGE, and Sanofi S.p.A, personal fees for acting as a speaker for Takeda S.p.A, AbbVie, and Sandoz S.p.A, and personal fees for acting on advisory boards for VSL3 and Eisai. A.A. received consulting and/or advisory board fees from AbbVie, Allergan, Amgen, Arena, Biogen, Bristol-Myers Squibb, Celgene, Celltrion, Ferring, Gilead, Janssen, Lilly, MSD, Mylan, Pfizer, Samsung Bioepis, Sandoz, Takeda; lecture and/or speaker bureau fees from AbbVie, Amgen, Biogen, Ferring, Gilead, Janssen, MSD, Mitsubishi-Tanabe, Nikkiso, Novartis, Pfizer, Sandoz, Samsung Bioepis, Takeda; and research grants from MSD, Pfizer, Takeda. All the other authors declare no conflict of interest.

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