Artificial intelligence (AI) is rapidly entering health care including safety-net, rural, and underserved settings, where access, capacity, and workforce constraints are most acute.
Across the literature, AI is described as a tool that can support clinical decision-making, improve outcomes, and expand access. But those same systems also raise concerns about bias, data limitations, and uneven impacts on underserved populations.
This Digging Deeper series draws from a systematic review of peer-reviewed research to explore both sides of that tension: what AI could make possible, and what risks must be addressed to ensure it does not reinforce existing inequities.
Literature Review Prepared by IC² Institute | The University of Texas at Austin
Article Authored by Briana Martin | EHF
The moment we’re in: AI is everywhere, fast
AI isn’t a future idea in health care anymore; it’s already here, and it’s evolving quickly. The literature describes this growth as “explosive,” driven by more available health data and advances in tools like machine learning and deep learning.
What that means in practice is that AI is showing up in more parts of the health system: helping analyze patient data, supporting diagnoses, improving imaging, and even assisting with treatment decisions. It’s also not just one thing. When we say “AI,” we’re really talking about a broad set of tools, some highly visible, others quietly embedded into workflows.
Why the stakes are different in safety-net, rural, and underserved care
The same trends look different depending on where you sit in the health system.
The literature points to rural and underserved communities in particular, where access to specialists is often limited, and providers are already stretched thin. In these settings, the need
for support is clear, but so are the constraints.
That creates a concerning tension. On one hand, AI has the potential to extend capacity and improve access. On the other hand, adopting new tools takes time, staffing, and infrastructure that may already be in short supply.
In other words: the places that could benefit most from AI may also face the biggest challenges in using it well.
The central tension: AI as a capacity-expander and a risk-multiplier
A consistent theme across the literature is that AI’s benefits and risks are closely connected.
AI is described as offering meaningful opportunities:
- Supporting clinicians with faster, more accurate decision making
- Improving health outcomes
- Helping overcome language barriers
- Potentially reducing certain forms of bias
But the same body of work also points to serious concerns:
- Algorithms that reflect or amplify existing inequities
- “Black box” systems that are difficult to interpret or question
- Gaps in regulation and oversight
- Data that doesn’t fully represent rural or underserved populations
Rather than treating these as separate issues, the literature suggests they’re part of the same story. The way AI is designed, trained, and implemented determines whether it helps close gaps or widens them.
Overview and methodology
To make sense of this fast-moving space, Episcopal Health Foundation partnered with the IC² Institute at The University of Texas at Austin to conduct a structured, systematic review of the literature.
Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the research team searched major databases, focusing on artificial intelligence in health care and its implications for equity, particularly in safety-net, rural, and underserved settings.
From a large pool of studies, the team narrowed in on literature reviews and supporting sources that directly addressed how AI is being used, where it shows promise, and where risks are most likely to emerge for underserved populations.
In total, the review draws on dozens of sources to synthesize key themes offering a grounded, evidence-based look at both the opportunities and the challenges of AI in health care.
What comes next in this series
Stay tuned for the next three posts:
- Where AI could help: Filling the Gaps that Safety-Net, Rural, and Underserved Systems Face Every Day
- How AI Deepens Inequity: Where the Risks Show Up in Practice
- What It Takes to Use AI Responsibly: Guardrails for Safety-Net, Rural, and Underserved Care
ATTRIBUTION & SOURCES
This post draws from Literature Review: AI in Safety-Net Health Care, prepared by the IC² Institute at The University of Texas at Austin.
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