# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Skill and Concern  
Although the future of AI in radiotherapy remains undecided, these approaches in oncology are limited by the ability of RT staff to understand the current conditions and development in the field, the accuracy of the methods, and the direction in which they are likely to unfold. Five recent surveys focused on the overall aspects of the AI application, from education to quality assurance. Among these, [PERSON] et al. [127] emphasized that in treatment planning AI improved the consistency in terms of planning optimization, productivity, and quality, allowing the staff to focus on patient care. This positive perceived impact of AI conflicts with the concern that it might cause a loss of skills or that there is a lack of training to maintain the planning expertise. The deep separation in technological availability among the developed and developing countries is also a factor that may cause judgmental errors [128]. Data from developed countries cannot simply be extrapolated and applied to developing countries without expecting discrepancies. Hence, ensuring equity in data representation, keeping in mind the geographical variations in diseases, populations, and health services, seems to be the way forward. As with all multifactorial problems complicated by the explosion of raw input data, there is a risk of cognitive overload [129]. Indeed, the radiation oncologist's evaluation is affected by the limited human cognitive capacity for using variables in the clinical decision-making process [129; 130]. On the other hand, the amount of information that computers can analyze is unmeasurable, and in any case is far beyond the human ability to retain information for specific clinical decisions. Therefore, AI is needed to identify early biomarkers for patient stratification or outcome prediction. However, several concerns in using AI tools as a \"black box\" solution have been raised by RT staff. One of the proposed methods to make ML and DL results more acceptable in clinical practice is to allow RT staff to understand the inner workings of the device they are using [131]. Continued education on AI is considered a priority by RT staff members, as well as the preservation of their skills (e.g., manual segmentation ability or contour supervision), as reported in several surveys [132; 133]. Thus, the role of medical professionals in RT departments is evolving thanks to the introduction of AI methods. Most of the staff's work will focus on macro-processes, namely verifying the system's performance quality, meaning staff competencies and education will need to change accordingly. In this context, novel tasks and roles to pursue are already included in the core curricula for RT staff according to the individual's professional role [134]. Also crucial for future implementation is that these AI tools might enable a more direct role in patient care responsibilities for all professionals, including medical physicists, through the online analysis of follow-up patient outcomes [135]. Caution must be observed in making decisions solely based on AI-generated information, the accuracy of which might be limited by the paucity of training data. The future use of AI will herald unprecedented changes in the field of radiation oncology. Numerous discussions have prompted careful thought about AI's impacts on the future landscape of RT, including how to preserve patient safety and how these devices and tools will be developed and regulated [136]. [PERSON] suggested that regulatory agencies mandate that ML developers disclose the information used in their algorithms [4]. Successful prospective validation through a large patient cohort may require academic-industrial-multicenter cooperation while fulfilling data security and intellectual property requirements. Cross-validated approaches among RT departments are also mandatory, and federated learning techniques might help rapidly implement shared AI solutions in RT [137]. With the federated learning technique, data are stored local centers.