The digital revolution in pathology: Towards a smarter approach to research and treatment

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

  • Francesco Tucci - , Università degli Studi di Milano, IRCCS Istituto Europeo di Oncologia - Milano (Autor:in)
  • Arvydas Laurinavicius - , Vilnius University (Autor:in)
  • Jakob Nikolas Kather - , Else Kröner Fresenius Zentrum für Digitale Gesundheit, Universität Heidelberg (Autor:in)
  • Catarina Eloy - , Universidade do Porto (Autor:in)

Abstract

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

Details

OriginalspracheEnglisch
Fachzeitschrift Tumori journal : TJ
Frühes Online-Datum12 Apr. 2024
PublikationsstatusElektronische Veröffentlichung vor Drucklegung - 12 Apr. 2024
Peer-Review-StatusJa

Externe IDs

PubMed 38606831
Mendeley 1eaf2213-f985-3cd9-895f-bf81078120d6

Schlagworte

Ziele für nachhaltige Entwicklung

ASJC Scopus Sachgebiete

Schlagwörter

  • Artificial intelligence, deep learning, digital health, digital pathology, machine learning, neural networks, omics, oncology