Using Artificial Intelligence for Optimization of the Processes and Resource Utilization in Radiotherapy

Abstract

The radiotherapy (RT) process from planning to treatment delivery is a multistep, complex operation involving numerous levels of human-machine interaction and requiring high precision. These steps are labor-intensive and time-consuming and require meticulous coordination between professionals with diverse expertise. We reviewed and summarized the current status and prospects of artificial intelligence and machine learning relevant to the various steps in RT treatment planning and delivery workflow specifically in low- and middle-income countries (LMICs). We also searched the PubMed database using the search terms (Artificial Intelligence OR Machine Learning OR Deep Learning OR Automation OR knowledge-based planning AND Radiotherapy) AND (list of Low- and Middle-Income Countries as defined by the World Bank at the time of writing this review). The search yielded a total of 90 results, of which results with first authors from the LMICs were chosen. The reference lists of retrieved articles were also reviewed to search for more studies. No language restrictions were imposed. A total of 20 research items with unique study objectives conducted with the aim of enhancing RT processes were examined in detail. Artificial intelligence and machine learning can improve the overall efficiency of RT processes by reducing human intervention, aiding decision making, and efficiently executing lengthy, repetitive tasks. This improvement could permit the radiation oncologist to redistribute resources and focus on responsibilities such as patient counseling, education, and research, especially in resource-constrained LMICs.

Revolutionizing radiation therapy: the role of AI in clinical practice

“…This may enable the same level of segmentation as with MRI, even in countries with limited medical resources that do not have MRI and must use only CT for treatment planning. The successful incorporation of AI into radiotherapy has the potential to standardize cancer treatment worldwide [ 98 ].…”

Artificial Intelligence in radiography education: A scoping review protocol. (Preprint)

“…Additionally, the National Health Service in the United Kingdom has also invested in AI technologies by creating a £21 million AI Diagnostic Fund to expedite the implementation of AI tools aimed at facilitating faster diagnosis and treatment for patients [7], indicating an effort to tap into the potential of these technologies to improve patient outcomes. AI health technologies are used to improve clinical decision-making [8], increase the accuracy of diagnosis [9,10], support pattern recognition and image interpretation [2], and dispense medication via robotic medication dispensing machines [11].…”