Ethics Principles for AI-Based Telemedicine for Public Health

Abstract and Introduction

Abstract

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. https://doi.org/10.2105/AJPH.2023.307225)

Introduction

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]

Box 1. Ethical Principles and Their Matched Themes for Artificial Intelligence (AI)-based Telemedicine for Public Health
Meaningful Ethical Principles for AI-Based Telemedicine for Public Health	Themes From AI Ethics Literature^17,19,29	Themes From Bioethics, Medical Ethics, Telehealth, and Telemedicine Literature^33–36	Themes From Public Health Ethics^37,38
Benevolence and nonmaleficence	Ensure AI safety, accuracy, and efficacy Empower human beings Prevent or minimize AI's mental or physical harm to individuals and groups	Benefit patient welfare Improve access to quality and interruption-free health care Do not cause pain or suffering	Gear effectiveness and efficiency toward reducing morbidity and mortality Create least infringement and least necessity of public justification Use noncoerciveness
Autonomy: "Respect people's decision-making"	Ensure meaningful human oversight Protect privacy and confidentiality Avoid manipulation and experimentation without informed consent	Respect human self-determination and decision-making at individual and relational (communal attachments) levels Provide good professional–patient relationship (clinicians' confidentiality) and fidelity	Ensure connectedness and solidarity, liberty, and self-determination
Justice and Fairness: "Treat people fairly"	Ensure inclusiveness, absence of bias, and equity in AI systems' access, use, design, data, and outcomes	Treat persons fairly, equitably, and appropriately: employ distributive justice and sociorelational justice Provide fair and balanced access	Ensure participation of public and the affected parties: procedural justice Distribute burdens and benefits fairly
Explicability	For technology robustness, ensure AI transparency, resilience, security, accuracy, reliability, and reproducibility Ensure AI intelligibility	Tell the truth	Disclose information and speak truthfully: transparency Avoid paternalism
Responsibility and Accountability	Ensure human warranty and third-party regulatory bodies' approval Implement harm redress mechanisms Ensure responsibility and liability		Ensure communal responsibility and community cohesiveness Be responsible to minimize burden on populations
Sustainability and Responsiveness	Ensure AI promotes health and environmental and work sustainability Ensure health protection and promotion		Ensure sustainability of the global environment and human systems: precautionary principle Ensure cost effectiveness and usefulness

Box 1. Ethical Principles and Their Matched Themes for Artificial Intelligence (AI)-based Telemedicine for Public Health

Meaningful Ethical Principles for AI-Based Telemedicine for Public Health

Themes From AI Ethics Literature^17,19,29

Themes From Bioethics, Medical Ethics, Telehealth, and Telemedicine Literature^33–36

Themes From Public Health Ethics^37,38

Benevolence and nonmaleficence

Ensure AI safety, accuracy, and efficacy
Empower human beings
Prevent or minimize AI's mental or physical harm to individuals and groups

Benefit patient welfare
Improve access to quality and interruption-free health care
Do not cause pain or suffering

Gear effectiveness and efficiency toward reducing morbidity and mortality
Create least infringement and least necessity of public justification
Use noncoerciveness

Autonomy: "Respect people's decision-making"

Ensure meaningful human oversight
Protect privacy and confidentiality
Avoid manipulation and experimentation without informed consent

Respect human self-determination and decision-making at individual and relational (communal attachments) levels
Provide good professional–patient relationship (clinicians' confidentiality) and fidelity

Ensure connectedness and solidarity, liberty, and self-determination

Justice and Fairness: "Treat people fairly"

Ensure inclusiveness, absence of bias, and equity in AI systems' access, use, design, data, and outcomes

Treat persons fairly, equitably, and appropriately: employ distributive justice and sociorelational justice
Provide fair and balanced access

Ensure participation of public and the affected parties: procedural justice
Distribute burdens and benefits fairly

Explicability

For technology robustness, ensure AI transparency, resilience, security, accuracy, reliability, and reproducibility
Ensure AI intelligibility

Tell the truth

Disclose information and speak truthfully: transparency
Avoid paternalism

Responsibility and Accountability

Ensure human warranty and third-party regulatory bodies' approval
Implement harm redress mechanisms
Ensure responsibility and liability

Ensure communal responsibility and community cohesiveness
Be responsible to minimize burden on populations

Sustainability and Responsiveness

Ensure AI promotes health and environmental and work sustainability
Ensure health protection and promotion

Ensure sustainability of the global environment and human systems: precautionary principle
Ensure cost effectiveness and usefulness

Ethics Principles for Artificial Intelligence–Based Telemedicine for Public Health

Abstract and Introduction

Abstract

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