AI-powered advances in type II endometrial cancer: global trends and African contexts

Abstract

Introduction: The advent of artificial intelligence (AI) in oncology has opened new avenues for enhancing the diagnosis, treatment, and prognosis of type II endometrial cancers (ECs), which account for the majority of EC-related deaths globally. With rising incidence and increasing concerns in Africa, type II ECs are often detected in advanced stages, exhibit aggressive progression, and resist conventional therapies. Despite these characteristics, they are still treated similarly to type I ECs, which are less aggressive and more treatment-responsive. Currently, no specific targeted therapies exist for type II ECs, creating an urgent need for innovative treatment options. Methods: This review examines the integration of AI-powered approaches in the care of type II ECs, focusing on their potential to address rising incidence and disparities in Africa. It explores AI-driven diagnostic tools, tailored therapeutic options, and ongoing innovative projects, including efforts to integrate indigenous knowledge into AI applications. Results: AI-powered therapeutic options tailored to the unique clinical profiles of type II EC patients show promise for developing targeted therapies. Several innovative projects are underway, leveraging AI to meet Africa's unique healthcare challenges. These applications demonstrate significant potential to reduce healthcare disparities and improve patient outcomes, especially in resource-limited settings. Discussion: This review highlights the transformative potential of AI technologies in improving the diagnosis, treatment and management of type II ECs, particularly in Africa, where healthcare disparities are significant. Through the integration of AI in the type II EC care continuum, challenges in African healthcare can be overcome. Innovative projects, leveraging AI to meet the continent's challenges, have the potential to improve patient outcomes. AI-driven therapies hold the key to personalized oncologic care, and indigenous African knowledge can be used to develop Afrocentric healthcare solutions. In Future, with continued research and the development of robust frameworks and transparent algorithms, investment and collaboration, the potential of AI in Type II EC will be realized.

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