Artificial Intelligence and the Right to Health: A Normative Framework for Emerging Health Technologies

ARTIFICIAL INTELLIGENCE and digital technologies are rapidly reshaping healthcare and public health worldwide. They offer immense promise for improving care, strengthening public health interventions, and creating more efficient processes. But their use has already led to harms, including discriminatory treatment of minority populations, unreliable clinical tools, and an ever-widening digital divide between wealthy countries and much of the Global South. 

Despite their promise and risks, legal and policy debates often focus narrowly on issues like safety and technical performance. They view the challenges posed by emerging health technologies as primarily technical problems that can be fixed by tweaking algorithms or recalibrating specifications. What is often missing is a broader normative and legal framework for evaluating health technologies that considers not only whether they work, but also how they will impact equity, access, and health-related rights.

This article argues that the right to health serves as a foundation for such a framework. The right to health centers critical values, illuminates the promise and risks of emerging health technologies, and offers guidance for their governance. First, I summarize the technologies and their advantages. Second, I address their risks and harms. Finally, I outline the right to health framework and apply it to a use case for digital adherence technologies.

AI in Healthcare: A Rapidly Expanding Landscape

AI and digital technologies now perform a wide range of functions across health systems. While they are often discussed as a single category, they are better understood as a set of distinct, but overlapping, applications.

Diagnostic and clinical decision-support systems analyze medical images and electronic health records to assist clinicians in identifying disease, recommending treatments, and assessing prognosis. These tools are already integrated into specialties such as radiology and pathology.

Predictive analytics and risk modeling systems use machine learning to forecast individual patient risk, such as disease progression or hospital readmission, as well as broader public health trends. These models increasingly shape decisions about resource allocation and intervention strategies.

Patient-facing digital health technologies, such as telemedicine platforms and AI-driven chatbots, enable patients to access health information and care remotely. In some settings, they are expanding access to services that would otherwise be unreachable for some populations.