Artificial Intelligence for Cancer Diagnosis and Treatment in Africa

This book provides a comprehensive exploration of how artificial intelligence and digital health innovations are reshaping cancer care across Africa. Beginning with the foundational epidemiological and health system realities of the continent, it examines Africa's readiness for oncology transformation and the ethical, legal, and social considerations of adopting AI in cancer diagnosis and treatment.

The book presents advanced applications, from deep learning-driven imaging and precision oncology to telepathology, mobile health platforms, and digital tools for survivorship, relapse prediction, and palliative care. It further highlights strategies for scaling AI systems, strengthening rural health infrastructure, fostering public-private partnerships, and building a skilled workforce equipped for the next era of oncology.

Designed as a timely resource for clinicians, cancer researchers, AI scientists, digital health innovators, public health professionals, policymakers, medical educators, and postgraduate students, this work bridges cutting-edge technology with urgent public health needs. It offers actionable frameworks, contextual adaptations for low-resource settings, and a forward-looking vision for equitable, AI-enabled cancer care in Africa. This book serves as both a guide and catalyst for sustainable, inclusive, and technologically empowered oncology systems across the continent.

Wasswa Shafik (Member, IEEE) is a Computer Scientist, Information Technologist, and Educator, serving as Research Director at the Dig Connectivity Research Laboratory (DCRLab), Kampala, Uganda. He earned a Bachelor's degree in Information Technology from Ndejje University (Uganda), a Master's in Information Technology Engineering (Communication and Computer Networks) from Yazd University (Iran), and a PhD in Digital Science (Computer Science) from the Universiti Brunei Darussalam (Brunei Darussalam). His research focuses on developing computationally and statistically efficient models and algorithms for complex artificial intelligence and machine learning challenges to support a sustainable future. His interests span Applied AI, Deep Learning, Smart Agriculture, Computer Vision, Digital Health and Education, Ecological Informatics, and Sustainable Computing. Shafik has authored, edited, and co-edited numerous books and published extensively in peer-reviewed journals, book chapters, and IEEE international conferences. He has taught and supported academic programs in Mathematics for Data Science, Advanced Topics in Computing, Advanced Algorithms, and Systems Performance and Evaluation. His professional experience includes roles in research, data management, and leadership across organizations such as PSI, TechnoServe, and Asmaah Charity Organisation.