## 1 Introduction  
Approximately 50% of all cancer patients are treated using radiotherapy, with approximately 40% of patients who receive curative treatment for cancer being within this figure [1]. A patient's post diagnosis radiotherapy pathway may be considered to pass through several stages including pre-treatment imaging, treatment planning (TP), simulation, radiotherapy accessory production, radiotherapy verification, radiation delivery, and patient monitoring [2]. Clinicians depend on the information extracted from the images taken prior to and during treatment to develop an appropriate treatment plan that may be adapted to anatomical changes during treatment. Therefore, enhanced imaging modalities resulting in more precise data may make the therapy more efficacious. Previously, technological advances have enabled clinicians to model the delivered radiation fields to the tumor shape and have led to advanced treatments, such as intensity modulated radiation therapy (IMRT) and volumetric modulated arc radiotherapy (VMAT) [3]. In this era, further software and technology developments allow rapid and consistent production of automated treatment planning [4]. There are many classifications of automated treatment planning, such as knowledge-based, expert-based, or AI based treatment planning [5]. An oncologist can look at approximately 2400 treatment planning cases in 10 years, while AI can start with 2400 treatment planning cases to train itself and reach millions of cases within a concise period of time [6]. AI, and more specifically machine learning, has also been proposed as a tool to increase automation and optimization of workflows in radiotherapy [7, 8, 9]. By implementing the concept of AI to interrogate the tremendous amount of treatment data and medical images available at any hospital, one may enable the delivery of improved stratified or personalized treatment [10, 11]. Information that may help with accurate decisions on radiation therapy treatment planning include a patient's tumor mass, height, weight, body mass index and previous exposure to radiation. Together with internal medical images of the patient that are obtained and analyzed, one may personalize the therapy by modifying different treatment planning parameters. AI has already been employed broadly in recent years in the medical field including data mining and knowledge discovery in medicine, medical expert systems, machine learning-based medical systems, and medical signal and image processing techniques [6; 12; 13]. Researchers have also demonstrated promising progress and a future for AI to be widely used in medical imaging, image segmentation, medical image registration, computer-aided detection (CAD) and diagnosis systems, as well as in treatment planning [5; 14; 15]. The radiotherapy treatment planning (TP) primary goal is reducing radiation dose to surrounding organs-at-risk (OAR) while maximizing dose to the tumor or target volume. This can be achieved by collecting accurate information about the tumor and the selected treatment, such as beam intensity shaping, planning margin, calculation of organ motion, therapy technique selection, and optimization of treatment planning [14]. An initial radiation treatment plan may not suit a patient well throughout the entire course of treatment due to, for example, post-surgical oedema, weight loss, or a change in tumor size or shape. Therefore, an adaptive plan may be created by acquiring a new set of images at some point over the treatment course and applying new parameters, e.g., new volumes or different prescription dose levels, for the remainder of the treatment. This process is called adaptive radiotherapy (ART). The need for quality assurance and methodical implementation of AI in clinical practice has been highlighted [16]. Furthermore, ART has yet to be fully implemented in clinical practice with questions still remaining on optimum practices, including when imaging should be undertaken to help with identification of patients requiring adaptive treatment plans [17; 18].