Adeoye, John, Akinshipo, Abdulwarith, Thomson, Peter, and Su, Yu Xiong (2022) Artificial intelligence-based prediction for cancer-related outcomes in Africa: status and potential refinements. Journal of Global Health, 12. 03017.

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Abstract

[Extract] Globally, cancer ranks among the most common causes of death, especially among people under 70 years of age [1]. With this burden rapidly increasing globally, utilizing prediction tools to assist decision-making and encourage individualized treatment planning is gradually becoming paramount in cancer diagnosis and management. Notably, many tools constructed on the backend of artificial intelligence (AI) algorithms have been shown to improve the predictive accuracy and clinical impact of risk prediction compared to clinical scenarios not utilizing these models [2]. However, the actualisation of the potential of health care AI has mostly been assessed in high-income and resource-driven centres. The impact and efficiency of oncological AI-based prediction tools are expected to be better realised when applied in low-resource and rural settings fraught with a paucity of experienced clinicians and specialists.

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