All About Implementation

This week, I took a deep dive into practically applying AI. For as much theoretical research and development that is conducted for AI, the application and deployment phase is even more important but often overlooked and underfunded. This is most evident in the medical field. While healthcare has lagged behind other industries in terms of embracing new technologies, such as digital recordkeeping, databases, and other advancements, the problems that face modern healthcare systems are too large to ignore. The research indicates the overall "messiness" and "holes" present in the medical records presents immense logistical challenges for current healthcare systems and workers. However, AI presents an opportunity to solve these issues. The most primary use cases of AI are providing structure to unstructured data and filling in gaps of incomplete information. This would make AI the perfect application for the medical field yet it is likely to be neglected into the future. If we want to see a future with a more efficient American healthcare system that reduces costs and improves convenience, then hospitals and research facilities need to embrace and deploy AI much faster than prior technologies. Also this week, I was able to talk to a Toyota enterprise architect who focuses on this integration of technology. He was able to overview the advancements in driving and general automation in corporations as well as the process of implementation. He made very strong points with applicable examples of implementation defining the success of a product. Ultimately, technology is a profit-driven industry and being able to provide the benefits of AI in a tangible manner to consumers or patients is the end goal of any research or experiments.