Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology

Simeng Zhu; Sung Jun Ma; Alexander Farag; Timothy Huerta; Mauricio Gamez; Dukagjin M Blakaj

doi:10.1016/j.hoc.2024.12.002

Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology

Hematol Oncol Clin North Am. 2025 Jan 7:S0889-8588(24)00156-4. doi: 10.1016/j.hoc.2024.12.002. Online ahead of print.

Authors

Simeng Zhu¹, Sung Jun Ma¹, Alexander Farag², Timothy Huerta³, Mauricio Gamez⁴, Dukagjin M Blakaj⁵

Affiliations

¹ Department 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.
² Department 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; Department of Otolaryngology-Head and Neck Surgery, Jacksonville Sinus and Nasal Institute, 836 Prudential Drive Suite 1601, Jacksonville, FL 32207, USA.
³ Department of Biomedical Informatics, 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.
⁴ Department of Radiation Oncology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
⁵ Division of Head and Neck/Skull Base, Department 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. Electronic address: [email protected].

PMID: 39779423
DOI: 10.1016/j.hoc.2024.12.002

Abstract

This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) and natural language processing (NLP) techniques. We examined CV-based AI/ML in digital pathology and radiomics, highlighting the prospective clinical studies demonstrating their utility. We also reviewed NLP-based AI/ML applications in clinical documentation analysis, knowledge assessment, and quality assurance. While acknowledging the challenges for clinical adoption, this review underscores the transformative potential of AI/ML in enhancing precision, efficiency, and quality of care in radiation oncology.

Keywords: Artificial intelligence; Big data; Computer vision; Machine learning; Natural language processing; Radiation oncology.

Publication types

Review