Translational Challenges of Implementing AI in Healthcare

Abstract

This chapter discusses the technical challenges and potential solutions for the implementation of Artificial Intelligence (AI) in healthcare. While some people may assume that AI in healthcare simply means replacing doctors with machines, the scope of AI in healthcare is much broader and includes, inter alia, virtual care delivery, digital health, intelligent automation, enhancing cognitive capabilities, and more. However, there are several barriers to entry for AI in healthcare, such as establishing trust, selecting the right use cases, dealing with data volume and privacy, and addressing bias in AI. The chapter proposes solutions such as investing in privacy-enhancing technologies, understanding how AI generates results, testing AI to prevent diagnostic errors, building up AI governance, utilizing innovative ways of data annotation, providing training and increasing engagement among healthcare workers, and educating to reduce patient reluctance. By considering these challenges and potential solutions, the healthcare industry can pave the way for the successful adoption of AI.

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