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Artificial intelligence in radiotherapy: Current applications and future trends.

Paul Giraud1, Jean-Emmanuel Bibault2

  • 1INSERM UMR 1138, Centre de Recherche des Cordeliers, 75006 Paris, France; Department of Radiotherapy, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.

Diagnostic and Interventional Imaging
|June 25, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances radiation therapy by improving treatment precision and efficiency. AI tools streamline workflows, increase practice homogeneity, and pave the way for personalized radiation oncology strategies.

Keywords:
Artificial intelligenceAutomatic segmentationClinical decision-supportRadiation oncologySynthetic imaging

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Computed tomography and intensity modulation have advanced radiation therapy, increasing treatment precision but also workflow complexity.
  • Accurate and uniform delineation of numerous volumes became critical with these technological advancements.
  • Improved computing power enabled reverse planning and 3D dose distribution generation.

Purpose of the Study:

  • To explore the role of artificial intelligence in enhancing radiation therapy workflows.
  • To investigate AI's potential in increasing practice homogeneity and efficiency.
  • To discuss the development of predictive tools for personalized radiation oncology.

Main Methods:

  • Implementation of artificial intelligence-based tools in routine radiation therapy practice.
  • Integration of workflow data with clinical and omic data.
  • Development of predictive models for clinical decision-making.

Main Results:

  • AI tools are increasing efficiency, reducing workload, and improving treatment homogeneity.
  • Data integration is enabling the development of proof-of-concept predictive tools.
  • These tools show potential for personalized oncologic strategies and dose prescriptions.

Conclusions:

  • Artificial intelligence offers significant opportunities to optimize radiation therapy workflows and enhance treatment delivery.
  • Predictive tools, though in early stages, are crucial for advancing towards personalized radiation oncology.
  • Prospective validation on large, multicenter cohorts is necessary for widespread adoption of AI-driven predictive models.