Fabio Ynoe de Moraes is an Assistant Professor and clinical investigator in Radiation-Oncology at the Queen’s University and Kingston Health Sciences Centre, Kingston ON, Canada. Dr. Moraes is a Brazilian graduated and trained Radiation Oncologist who moved to Canada in 2016 for a 3 years fellowship at the University of Toronto (Princess Margaret Cancer Centre). During his career, he performed high level clinical and research activities at the Princess Margaret Cancer Centre and the Memorial Sloan Kettering Cancer Centre (NY USA). Dr. Moraes has more than 60 published peer-reviewed articles, including high impact journals such as Lancet Oncology, Nature Biotechnology, New England Journal of Medicine among others. He has been awarded multiple distinctions including being a young leader of the National Academy of Medicine of Brazil, a young leader of the Union of International Cancer Control, and a couple of research distinctions for the University of Toronto and Princess Margaret Cancer Centre, Canada. Dr. Moraes is an associate editor for the Journal of Oncology Practice (ASCO), and a clinical advisor for the Cochrane Collaboration (UK). His research focus on CNS tumors (including spine), global oncology and the application of radiotherapy innovations and AI/ML methodology in the clinical practice / healthcare systems.
Recent advances in computer technology, infrastructure and accessibility to highly powerful computers globally have nurtured expectation on new health related artificial intelligence (AI) applications. It has generated a hype that AI will tackle all challenges exclusive to the field of global health and accelerate achievement of the health-related sustainable development goals. However, multiple fundamental question and concerns about AI-driven health care are to be addressed. On this scenario, discussing the use of artificial intelligence (AI) for improving global health, including oncology, is mandatory.
Implementation of AI has already begun for a broad range of common health issues, medical procedures and healthcare processes. AI-based health interventions fit into 5 major categories relevant to global health researchers: (1) diagnosis, (2) patient risk assessment, (3) disease outbreak prediction, surveillance and monitoring (4) health policy and planning and (5) overarching innovation. But besides, the clear potential niches for research and applications, the use of AI in health care lacks clear ethics, regulatory and practical guidelines for deployment at scale. Thus, the health community will need to work rapidly to understand applications, establish ethical based guidelines for development, testing, and use, and promote a user-driven centered agenda to facilitate meaningful, equitable and ethical use of AI.