Current Challenges and Barriers to Real-World Artificial Intelligence Adoption for the Healthcare System, Provider, and the Patient.

Abstract

Artificial intelligence (AI), or the use of automated systems that display the ability to correctly interpret, to learn from, and to achieve specific goals by use of external data, is an emerging technology that has myriad implications for changing the way we interact with the world. Although this technology is already being used in many fields such as banking, retail, and education, AI has the potential to transform other fields including healthcare. Within healthcare, ophthalmology is uniquely positioned to benefit from AI not only through clinical decision support technology but also through improved image processing innovations such as real-time segmentation, automated image quality improvements, and assisted or autonomous disease screening tools.1,2 Although there are now Food and Drug Administration–approved technologies within ophthalmology, such as IDx-DR (Coralville, IA, USA) for early diagnosis of diabetic retinopathy and diabetic macular edema, numerous challenges still exist to realize the potentially transformative impact of these technologies in day to day practice. The need for the ophthalmology community to take a thoughtful approach to AI innovation and implementation is accentuated by the high stakes involved. The impact of misleading patients and clinicians on a health condition is much greater than a retail store misinterpreting the next book you may like to buy. As a result, we need to increase discussion about the issues surrounding who, what, when, how, and why we might use AI in practice, including ethical and liability considerations, to determine how best to implement AI for all stakeholders including practitioners, patients, practices/hospitals, and industry. This article aims to highlight the challenges and barriers to real-world AI adoption that impact the technology's utility. We examine these specific challenges that will face health care organizations, providers, and patients.