Can artificial intelligence improve cancer treatments?

Derbal Y.

Health Informatics Journal, vol.28, no.2, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1177/14604582221102314
  • Journal Name: Health Informatics Journal
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, CINAHL, Computer & Applied Sciences, EBSCO Education Source, Educational research abstracts (ERA), EMBASE, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), MEDLINE, Directory of Open Access Journals
  • Keywords: artificial intelligence, cancer treatment, clinical decision-making, machine learning, reinforcement learning
  • TED University Affiliated: No

Abstract

© The Author(s) 2022.Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.