Chapter 13 - The rise of AI in telehealth

Abstract

Since its introduction in 1955, artificial intelligence (AI) has continued its growth and expansion across all industries and societal sectors. It took the COVID-19 pandemic for AI and its subsets to take the center stage in medicine and health care. AI is a broad discipline and encompasses machine learning (ML), deep learning (DL), and other techniques. Advancements in AI enabled, facilitated, and accelerated the expansion of telehealth. Telehealth describes the wide array of digital information and communication technologies and systems that allow the delivery of health and health-related services. There are three distinct subtypes of telehealth: synchronous, asynchronous, and remote (tele) monitoring. The overarching goal of telehealth is to break down barriers in delivery of high value care by overcoming challenges resulting from time or location constraints. The end goal is not to replace in-person care, rather to commoditize and democratize high quality, high value care. On the other hand, there remain significant limitations and pitfalls, particularly regulatory and technological. Examples include best practice guidelines on the adaptation of standards regulating data exchange, expansion of reimbursement and importantly ethical challenges. The latter include critical issues such as data privacy, security, and governance, AI-introduced bias, the black box nature of some AI/ML algorithms and the impact of AI technologies/algorithms on health disparities and inequities. Disparities in access to and use of tele-health were already known but highlighted during the COVID-19 pandemic. Recognition of this hurdle led to the emerging and rapidly growing field of digital determinants of health, which comprise factors like digital literacy, access to AI/technology, and community infrastructure like access to WiFi/broadband internet.