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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence

Background:

  • The increasing volume of data in healthcare has led to a surge in machine learning (ML) applications within radiation oncology.
  • Despite numerous potential applications, including treatment response modeling, planning, and image guidance, clinical integration of ML remains limited.

Purpose of the Study:

  • To accelerate the adoption of machine learning in radiation oncology by detailing its benefits and potential.
  • To identify and discuss current challenges and future research directions in the field.
  • To provide guidance for practitioners and newcomers on implementing ML tools effectively and reporting results transparently.

Main Methods:

  • This white paper synthesizes current knowledge and expert perspectives on machine learning in radiation oncology.
  • It reviews existing and potential applications of ML algorithms.
  • It outlines challenges and proposes recommendations for clinical implementation and research.

Main Results:

  • Machine learning presents numerous opportunities for advancing radiation oncology practices.
  • Significant barriers hinder the widespread clinical adoption of these technologies.
  • Clear guidelines are needed for effective implementation and transparent reporting.

Conclusions:

  • Machine learning holds immense promise for revolutionizing radiation oncology, but practical implementation requires addressing current challenges.
  • Further research and standardized reporting guidelines are essential for realizing the full potential of ML in clinical settings.
  • This paper serves as a guide for medical physicists and radiation oncologists navigating the integration of ML into practice.