Ethics Principles for Artificial Intelligence–Based Telemedicine for Public Health

Simona Tiribelli, PhD; Annabelle Monnot, MSc; Syed F. H. Shah, BA, MB BChir; Anmol Arora, MB BChir; Ping J. Toong; Sokanha Kong, MPhil


Am J Public Health. 2023;113(5):577-584. 

In This Article

Abstract and Introduction


The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems.

Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health.

We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577–584.


The increasingly widespread availability of digital devices has facilitated the growth of telehealth and telemedicine over the past few decades, which is broadly described by the US Centers for Disease Control and Prevention (CDC) as "the use of electronic information and telecommunication technologies to support and promote long-distance clinical health care, patient and professional health-related education, public health, and health administration."[1] In particular, the development and adoption of telehealth and telemedicine were exponentially accelerated by the COVID-19 pandemic, with the CDC reporting a 50% and 154% increase in teleconsultations in January 2020 and March 2020, respectively, compared with the same periods in 2019. This was arguably the most substantial and large-scale proof of the value of telehealth and telemedicine in ensuring prevention and other health services for diverse communities worldwide—especially in times of global health crises[2]—and proof of their potential benefit for global public health.[3]

In parallel, the field of artificial intelligence (AI) and its applications to the health and medical domains have been expanding rapidly because of improvements in the hardware capabilities of modern computer systems, the pervasive applications of information and communication technologies (ICTs), and the consequent digitalization of health data and records. "AI" is an umbrella term conventionally used to refer to the ability of computer systems to perform tasks that are usually thought to require human skills, including reasoning and self-correction.[4]

AI and, specifically, probabilistic machine learning (ML) algorithms—models that learn novel correlations and patterns from the collection and mining of huge streams of data to execute tasks, decisions, and predictions—are now being developed in nearly all domains of public health and medical practice.[4] These include, but are not limited to, diagnostic support, remote monitoring, prediction and detection, robot-assisted treatments, and biomedical research.[5] In all these diverse applications, ML presents a huge opportunity to supplement or enhance remote health services via telehealth devices and telemedicine solutions, which we particularly address in this article. Indeed, although telemedicine and telehealth overlap greatly and many prefer to use such terms interchangeably, there is an often undervalued but key distinction between the 2 domains, which experts in the field and public health bodies such as the World Health Organization (WHO) also advocate.[6,7]