Artificial intelligence and radiotherapy: Evolution or revolution?

Journal: Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PMID:

Abstract

The integration of artificial intelligence, particularly deep learning algorithms, into radiotherapy represents a transformative shift in the field, enhancing accuracy, efficiency, and personalized care. This paper explores the multifaceted impact of artificial intelligence on radiotherapy, the evolution of the roles of radiation oncologists and medical physicists, and the associated practical challenges. The adoption of artificial intelligence promises to revolutionize the profession by automating repetitive tasks, improving diagnostic precision, and enabling adaptive radiotherapy. However, it also introduces significant risks, such as automation bias, verification failures, and the potential erosion of clinical skills. Ethical considerations, such as maintaining patient autonomy and addressing biases in artificial intelligence systems, are critical to ensuring the responsible use of artificial intelligence. Continuous training and development of robust quality assurance programs are required to mitigate these risks and maximize the benefits of artificial intelligence in radiotherapy.

Authors

  • Charlotte Robert
    U1030 Molecular Radiotherapy, Paris-Sud University - Gustave Roussy - Inserm - Paris-Saclay University, Villejuif, France; Department of Medical Physics, Gustave Roussy - Paris-Saclay University, Villejuif, France. Electronic address: ch.robert@gustaveroussy.fr.
  • Philippe Meyer
    Department of Medical Physics, Paul Strauss Center, Strasbourg, France; ICube-UMR 7357, Strasbourg, France. Electronic address: pmeyer@strasbourg.unicancer.fr.
  • Brigitte Seroussi
    Sorbonne Universités, UPMC Université Paris 06, UMR_S 1142, LIMICS, Paris, France.
  • Thomas Leroy
    Radiation department, Clinique Les Dentelières, Valenciennes, France.
  • Jean-Emmanuel Bibault
    Radiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France; INSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France. Electronic address: jean-emmanuel.bibault@aphp.fr.