Abstract:

Artificial intelligence (AI) is a computer science that tries to imitate human-like intelligence on machines using computer software and algorithms without direct human stimuli to perform certain tasks. Machine learning (ML) is the subunit of AI that uses data-driven algorithms that learn to emulate human behavior based on a previous example or experience. Deep learning (DL) is an ML technique that utilizes deep neural networks to construct a model. The growth and sharing of data, increased computing power, and developments in ML have initiated a transformation in healthcare. Advances in radiation oncology have generated substantial data that must be integrated with computed tomography (CT) imaging, dosimetry, and other imaging modalities before each fraction. There are many algorithms used in Radiation Oncology. Each of these methods has advantages and limitations and different computing requirements. In this paper, we aimed to review the radiotherapy (RT) process by identifying the specific areas in which the quality and efficiency of ML can be increased and a workflow chart can be created. The RT stage is divided into seven groups as patient assessment, simulation, contouring, planning, quality assessment (QA), treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations and advantages of ML algorithms was performed at each stage.

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