Overview of AI in Rural Healthcare

AI (artificial intelligence) refers to technologies that allow computers to perform tasks that typically require human intelligence. AI is an umbrella term that includes many different technologies, models, tools, and approaches.

This toolkit focuses on commonly used forms of AI in healthcare, often called "narrow AI." Narrow AI is designed to perform specific tasks. It is a tool that supports healthcare providers rather than replacing their clinical expertise and judgment.

AI, as defined by the Office of the National Coordinator for Health Information Technology,

“Enables computer systems to perform tasks normally requiring human intelligence — for example, recognizing patterns, learning from experience, drawing conclusions, making predictions, etc.”

AI has been part of healthcare for decades. AI was first introduced in the 1950s to play the game of checkers and soon emerged in clinical settings. Different types of AI systems were developed to support clinical decision-making in areas such as internal medicine, mental health, and infectious disease. The Congressional Research Service report, Artificial Intelligence in Healthcare, is a key resource that explains the origins of AI in healthcare and considerations for safety and transparency.

In healthcare, AI is used to support providers, care teams, and patients. AI technologies are trained on large amounts of health data — such as patient demographic information, medical records, clinical notes, labs, device readings, and images — to identify patterns, generate insights, and make predictions and recommendations.

In rural healthcare settings, AI technologies can help reduce the burden of repetitive tasks, improving administrative efficiency and allowing busy providers to spend more time focused directly on their patients.

Types of AI

There are many different AI systems, tools, models, platforms, and products. Most fall under narrow AI, which is used to perform specific tasks and is the primary focus on this toolkit. There are several sub-fields and systems within narrow AI, such as machine learning. Machine learning is a technique that helps computers learn patterns in data and make predictions without being explicitly programmed for each task. Two common types of machine learning are:

Natural language processing (NLP) – Helps computers understand, interpret, and generate human language, such as clinical notes or patient messages.

Computer vision (CV) – Helps computers analyze and learn from visual data, such as X-rays, medical images, and videos.

A newer class of narrow AI is generative AI (or GenAI), which uses patterns learned from large amounts of data to create new content, such as text, images, or audio. GenAI systems are based on large language models (LLMs). GenAI is being used in healthcare, though there are risks related to accuracy, privacy, and bias, that healthcare organizations must consider and address.

AI can improve efficiency and decision-making for rural healthcare providers and administrators. By addressing core challenges in rural areas, including geographic distance, workforce shortages, and limited access to specialty care, AI has the potential to strengthen rural healthcare delivery, increase efficiency, and expand access to services.

AI Levels of Autonomy

In practice within healthcare settings, AI tools can have different levels of autonomy or independence:

Assistive – Technology assists the healthcare provider and does not provide any independent analysis.

Augmentative – Technology analyzes data, requiring healthcare provider interpretation.

Autonomous – Technology can independently review data and generate conclusions without a healthcare provider.

Examples of AI Tasks in Healthcare

AI can support both clinical and operational healthcare tasks. Clinical uses may include:

Clinical decision support
Transcribing, summarizing, and reporting information
Patient monitoring

Operational uses may include:

Automating routine tasks
Notetaking
Scheduling
Billing and coding

Resources to Learn More

AI in Rural Health
Document
Offers rural clinics and providers an overview of AI, and its applications to enhance patient care.
Organization(s): National Consortium of Telehealth Resource Centers
Date: 12/2024

Artificial Intelligence and the Future of Work
Document
Provides an overview of AI, including its history, approaches such as machine learning and natural language processing, and implications for work.
Organization(s): MIT Task Force on the Work of the Future, Massachusetts Institute of Technology
Authors: Malone, T.W., Rus, D., & Laubacher, R.
Date: 12/2020

FDA Digital Health and Artificial Intelligence Glossary - Educational Resource
Website
Compiles commonly used terms related to technologies and AI in health and healthcare.
Organization(s): U.S. Food and Drug Administration

Health Care Artificial Intelligence: Foundation
Document
Describes how AI is used in clinical care, public health, and research. Covers the laws, levels of autonomy, various applications, significant risks, and the federal governing bodies.
Organization(s): California Telehealth Resource Center
Date: 5/2025

Health Care Artificial Intelligence: Governance Guide
Document
Provides guidance on AI policy, procedures, personnel, testing, and regulatory compliance for community clinics and Critical Access Hospitals implementing AI.
Organization(s): California Telehealth Resource Center
Date: 5/2025

Health Care Artificial Intelligence: Unintended Impact
Document
Identifies key steps to assess and mitigate risk of unintended impacts of AI systems in healthcare, including a pre-deployment assessment, deployment, post-deployment assessment, and continuous improvement activities.
Organization(s): California Telehealth Resource Center
Date: 5/2025

Healthcare Artificial Intelligence: C-Suite Overview
Document
An AI half-day retreat agenda specifically designed to support rural and community health executives, management teams and board members. Offers resources that are AI specific including a vision statement, governance charter, communication plan for staff and board and workforce literacy training strategy.
Organization(s): California Telehealth Resource Center
Date: 12/2025