Artificial intelligence in oncology: From bench to clinic.

Journal: Seminars in cancer biology
Published Date:

Abstract

In the past few years, Artificial Intelligence (AI) techniques have been applied to almost every facet of oncology, from basic research to drug development and clinical care. In the clinical arena where AI has perhaps received the most attention, AI is showing promise in enhancing and automating image-based diagnostic approaches in fields such as radiology and pathology. Robust AI applications, which retain high performance and reproducibility over multiple datasets, extend from predicting indications for drug development to improving clinical decision support using electronic health record data. In this article, we review some of these advances. We also introduce common concepts and fundamentals of AI and its various uses, along with its caveats, to provide an overview of the opportunities and challenges in the field of oncology. Leveraging AI techniques productively to provide better care throughout a patient's medical journey can fuel the predictive promise of precision medicine.

Authors

  • Jamal Elkhader
    HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA; Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA.
  • Olivier Elemento
    Institute for Precision Medicine.