IMPACT OF AI ON HEALTHCARE ACCESS IN UNDERSERVED POPULATIONS

Abstract

This study investigate the potential implications of AI in relation to healthcare equity and disparities, with an aim of expanding the understanding of how this emerging technology can both enhance and hinder the quintessential goal of health equity. It is also important to note that those who are in the vulnerable categories such as the rural dwellers, the minorities, those with low incomes, immigrants, and many others often have poor access to these resources and technology in the healthcare sector. Technologies such as machine learning, natural language processing, robotics and many others might provide opportunities in extending access through telemedicine, diagnostic tools, individualized treatment options and much more. However, most AI systems are designed with databases from majority or privileged population. Therefore, the utilization of such biased datasets not only hinders the construct validity but also may increase disparities. Despite the benefits that AI can bring to reduce the pressure on understaffed and underfunded health systems, integration has to be done with care not to contribute to further entrenching of inequality. This means that researchers need to focus on ID and I&D when developing and evaluating AI systems for different groups. The government must identify areas where it will take time for the targeted population to accept the technology because of the following challenges; literacy, access to the Internet, language, and trust, among others. In order to progress in addressing the issues we must involve multiple sectors and members of the community to fully understand the needs and risks involved as well as develop solutions for implementation. When applied to deployment centers, equity and representation, AI can overcome infrastructure deficits and provide those who need it with the information, providers, and care. By virtuous and ethical use of technology, equal health for all people of the world is possible.