Artificial intelligence, ChatGPT, and other large language models for social determinants of health: Current state and future directions

Abstract

This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global problem exacerbated by the COVID-19 pandemic. We provide a broad discussion on current developments in digital health and artificial intelligence (AI), including large language models (LLMs), as transformative tools in addressing SDOH factors, offering new capabilities for disease surveillance and patient care. Simultaneously, we bring attention to challenges, such as data standardization, infrastructure limitations, digital literacy, and algorithmic bias, that could hinder equitable access to AI benefits. For LLMs, we highlight potential unique challenges and risks including environmental impact, unfair labor practices, inadvertent disinformation or "hallucinations," proliferation of bias, and infringement of copyrights. We propose the need for a multitiered approach to digital inclusion as an SDOH and the development of ethical and responsible AI practice frameworks globally and provide suggestions on bridging the gap from development to implementation of equitable AI technologies.

Figure 1

Reinforcement learning with human in the loop

Figure 2

The interconnectedness of SDOH data sources Individual patient-level data flow into neighborhood- and population-level data sources such as population census reports.

Figure 3

A multitiered approach for digital inclusion using WHO SDOH framework The framework was adapted from Van Dijk.