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Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology.

Simeng Zhu1, Sung Jun Ma1, Alexander Farag2

  • 1Department of Radiation Oncology, The Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, The Ohio State University Comprehensive Cancer Center, 460 West 10th Avenue, Columbus, OH 43210, USA.

Hematology/Oncology Clinics of North America
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and machine learning (AI/ML) are revolutionizing radiation oncology. These technologies, including computer vision and natural language processing, promise to improve precision and efficiency in patient care.

Keywords:
Artificial intelligenceBig dataComputer visionMachine learningNatural language processingRadiation oncology

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Radiation oncology faces challenges in data analysis and clinical workflow efficiency.
  • Integrating advanced computational techniques is crucial for progress.

Purpose of the Study:

  • To review the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology.
  • To focus on computer vision (CV) and natural language processing (NLP) techniques.
  • To assess the clinical utility and potential of AI/ML in the field.

Main Methods:

  • Literature review of AI/ML applications in radiation oncology.
  • Examination of CV-based AI/ML in digital pathology and radiomics.
  • Analysis of NLP-based AI/ML in clinical documentation, knowledge assessment, and quality assurance.

Main Results:

  • CV-based AI/ML shows utility in digital pathology and radiomics through prospective clinical studies.
  • NLP-based AI/ML demonstrates applications in analyzing clinical documentation, assessing knowledge, and ensuring quality.
  • Identified challenges exist for widespread clinical adoption.

Conclusions:

  • AI/ML, particularly CV and NLP, holds transformative potential for radiation oncology.
  • These technologies can significantly enhance precision, efficiency, and quality of care.
  • Overcoming adoption challenges is key to realizing the full benefits of AI/ML in clinical practice.