N. Khelassi-Toutaoui

BACKGROUND: There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery. METHODS: To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance. RESULTS: We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space. CONCLUSION: The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.

Abstract The International Atomic Energy Agency (IAEA) developed a staffing model to estimate the number of clinically qualified medical physicists (CQMP) that are required in an imaging facility, including diagnostic radiology and nuclear medicine. For the first time this staffing model was applied on a large scale across Africa. Within the framework of the IAEA African Regional Agreement (AFRA) Technical Cooperation (TC) project RAF6/053 entitled “Enhancing Capacity Building of Medical Physics to Improve Safety and Effectiveness of Medical Imaging (AFRA)”, a survey based on the IAEA staffing model was used to investigate the current CQMP workforce needs in imaging and radionuclide therapy in Africa in order to establish a baseline, identify gaps and suggest steps for improvement. The survey was open for five months, after which data verification was performed. 82 responses were received from 21 countries, including data from 97 diagnostic radiology and 40 nuclear medicine departments, as well as 75 interventional radiology departments and/or catheterization laboratories. Only 26·8% of centres employed an adequate number of CQMPs. The staffing model indicated that 134·3 CQMPs were required for these centres, but only 63 are currently employed in medical imaging and/or nuclear medicine at these centres. At least 11 countries do not have a single institution with an adequate number of CQMPs. Data analysis indicated that the number of radiology and nuclear medicine CQMPs is largely inadequate, at least by a factor of 20 in almost all countries in the region.