Africa’s readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way

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dc.contributor.authorManson, E.N.
dc.contributor.authorHasford, F.
dc.contributor.authorSosu, E.K.
dc.contributor.authoret al.
dc.date.accessioned2023-09-29T10:07:40Z
dc.date.available2023-09-29T10:07:40Z
dc.date.issued2023
dc.descriptionResearch Articleen_US
dc.description.abstractBackground: 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 tech nology, 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 radio therapy 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 Af rican 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.en_US
dc.identifier.otherdoi.org/10.1016/j.ejmp.2023.102653
dc.identifier.urihttp://ugspace.ug.edu.gh:8080/handle/123456789/40179
dc.language.isoenen_US
dc.publisherPhysica Medicaen_US
dc.subjectArtificial intelligenceen_US
dc.subjectRadiotherapyen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectQuality assuranceen_US
dc.titleAfrica’s readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the wayen_US
dc.typeArticleen_US

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