Edem Sosu

PURPOSE: The Global Medical Physics Training and Development Program (GMPTDP) is a novel initiative that provides United States (US)-based graduate students in medical physics with structured, immersive clinical training in Ghana. METHODS: The five-week program begins with a cultural and clinical orientation in the US, followed by 4 weeks of clinical rotations across leading Ghanaian medical institutions. During rotations, students gain experience with teletherapy (LINACs and cobalt-60), brachytherapy, treatment planning, imaging, and more. Trainees participate in clinical activities, conduct collaborative projects, and engage in community outreach and cultural immersion. The program culminates in a symposium highlighting student experiences and future directions with speakers including physicists, oncologists, engineers, and policymakers. RESULTS: The pilot year of the program was successfully completed by three students from May 28 2024-July 2 2024. This article outlines the development, structure, and implementation of GMPTDP as a replicable model for global health training in medical physics, emphasizing sustainable partnerships between high-income and low- and middle-income countries. Educational objectives include demonstrating effective cross-border training models, fostering collaborative research, and expanding global clinical experience in the field of medical physics. CONCLUSIONS: A model for a global medical physics training program was developed and successfully implemented.

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.