Artificial intelligence for precision medicine in autoimmune liver disease

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Alessio Gerussi - , University of Milan - Bicocca, Azienda Ospedaliera San Gerardo Monza (Author)
  • Miki Scaravaglio - , University of Milan - Bicocca, Azienda Ospedaliera San Gerardo Monza (Author)
  • Laura Cristoferi - , University of Milan - Bicocca, Azienda Ospedaliera San Gerardo Monza (Author)
  • Damiano Verda - , Rulex Inc (Author)
  • Chiara Milani - , University of Milan - Bicocca, Azienda Ospedaliera San Gerardo Monza (Author)
  • Elisabetta De Bernardi - , University of Milan - Bicocca (Author)
  • Davide Ippolito - , Azienda Ospedaliera San Gerardo Monza (Author)
  • Rosanna Asselta - , IRCCS Istituto Clinico Humanitas - Rozzano (Milano), Humanitas University (Author)
  • Pietro Invernizzi - , University of Milan - Bicocca, Azienda Ospedaliera San Gerardo Monza (Author)
  • Jakob Nikolas Kather - , Else Kröner Fresenius Center for Digital Health, RWTH Aachen University (Author)
  • Marco Carbone - , University of Milan - Bicocca, Azienda Ospedaliera San Gerardo Monza (Author)

Abstract

Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.

Details

Original languageEnglish
Article number966329
JournalFrontiers in immunology
Volume13
Publication statusPublished - 11 Nov 2022
Peer-reviewedYes

External IDs

PubMed 36439097

Keywords

ASJC Scopus subject areas

Keywords

  • autoimmunity, deep learning, digital pathology, genomics, machine learning, population genetics, radiomics, whole-slide digital image analysis