ABSTRACT
Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.
ABSTRACT
Diagnostics, including laboratory tests, medical and nuclear imaging, and molecular testing, are essential in the diagnosis and management of cancer to optimize clinical outcomes. With the continuous rise in cancer mortality and morbidity in the Association of Southeast Asian Nations (ASEAN), there exists a critical need to evaluate the accessibility of cancer diagnostics in the region so as to direct multifaceted interventions that will address regional inequities and inadequacies in cancer care. This paper identifies existing gaps in service delivery, health workforce, health information systems, leadership and governance, and financing and how these contribute to disparities in access to cancer diagnostics in ASEAN member countries. Intersectoral health policies that will strengthen coordinated laboratory services, upscale infrastructure development, encourage health workforce production, and enable proper appropriation of funding are necessary to effectively reduce the regional cancer burden.
