Artificial Intelligence in radiotherapy: state of the art and future directions.

Journal: Medical oncology (Northwood, London, England)
Published Date:

Abstract

Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients' medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a "black box" in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.

Authors

  • Giulio Francolini
    IFCA Radiotherapy and Medical Physics Unit, Via del Pergolino, 1, 50139 Florence, Italy.
  • Isacco Desideri
    Radiotherapy Department, University of Florence, Florence, Italy. isacco.desideri@unifi.it.
  • Giulia Stocchi
    Radiotherapy Department, University of Florence, Florence, Italy.
  • Viola Salvestrini
    Radiotherapy Department, University of Florence, Florence, Italy.
  • Lucia Pia Ciccone
    Radiotherapy Department, University of Florence, Florence, Italy.
  • Pietro Garlatti
    Radiotherapy Department, University of Florence, Florence, Italy.
  • Mauro Loi
    Radiotherapy Department, University of Florence, Florence, Italy.
  • Lorenzo Livi
    Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, United Kingdom.