Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology - 25/02/25

Doi : 10.1016/j.hoc.2024.12.002

Simeng Zhu, MD ^a,^{^{1
Contributed equally.}}, Sung Jun Ma, MD ^a,^{^{1
Contributed equally.}}, Alexander Farag, MD ^a,^b, Timothy Huerta, PhD, MS ^c, Mauricio E. Gamez, MD ^d, Dukagjin M. Blakaj, MD, PhD ^e,^⁎
^a 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
^b Department of Otolaryngology-Head and Neck Surgery, Jacksonville Sinus and Nasal Institute, 836 Prudential Drive Suite 1601, Jacksonville, FL 32207, USA
^c 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
^d Department of Radiation Oncology, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA
^e 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

^∗Corresponding author. 460 West 10th Avenue, D252N, Columbus, OH 43210.460 West 10th AvenueD252NColumbusOH43210

Résumé

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.

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

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Vol 39 - N° 2

P. 453-469 - avril 2025 Retour au numéro

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Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology - 25/02/25

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