Health Disparities and Artificial Intelligence: A Remedy, Problem or Both?
A new textbook that presents fresh insights and solutions for achieving health equity also shares some of the first public health perspectives on the application of artificial intelligence to reduce health disparities.
Achieving Health Equity: Context Controversies and Remedies (Jones & Bartlett Learning, 2026) is replete with solutions for achieving greater health equity for all people in the United States. One of these solutions is artificial intelligence, which presents both new opportunities and potential problems.
That’s why it’s imperative for public health professionals to get a solid understanding of the AI landscape. Artificial intelligence is no longer a futuristic goal. It is here and it is now being used in almost every industry, including healthcare.
How Can AI Offer Remedies Toward Achieving Health Equity?
One of the primary issues preventing health equity is impeded access to care. Through application of AI, healthcare institutions can use approaches such as telemedicine to reach areas of the country where healthcare is limited. This has the potential to expand healthcare services to patients in more geographic locations, including rural communities.
But telemedicine does not resolve the problem completely because patients need health insurance to access it. This is an ongoing issue in the United States due to high insurance costs, Medicaid and Medicare requirements, and unemployment. For these reasons, virtual care options that incorporate AI may simply not be available to many people.
Another factor is the digital divide. To use telemedicine platforms, chatbots and symptom checkers with AI features, people need computers. For more patients to benefit from AI possibilities such as this, it’s imperative to ensure access to appropriate technology.
On the other end of the patient-provider interaction, healthcare clinicians have expanded technology access to offer such services. They can also utilize other aspects of AI to assist with their approaches to working with patients/customers to achieve optimum health. For example, AI algorithms can be used in imaging and pathology.
New Book Meets the Needs of Today’s Students
The book Achieving Health Equity: Context, Controversies and Remedies prepares public health students and professionals for this rapidly advancing technology landscape by defining current terms related to AI use in healthcare and providing algorithms and other resources in-text and in the glossary.
Algorithms that incorporate AI may be helpful as long as bias is mitigated. One of the problems with AI approaches in terms of algorithms and predicative analytics (e.g., predicting chronic health conditions such as heart disease, especially among people who are experiencing low incomes) is that inherent biases may shape or determine AI responses.
For example, assuming that certain chronic diseases are genetically inherent (a common but inaccurate conclusion about Black people compared to White people in the U.S.) without looking at factors such as socioeconomic status, environment, commercial determinants, etc., tarnishes predictability.
The question then becomes: Who is training AI mechanisms and algorithmic approaches?
The Role of AI Beyond Clinical Care
Beyond clinical care, how can AI serve as a remedy, particularly in terms of public health? One approach and a strong example is via the analysis of social determinants of health (SDOH).
Achieving Health Equity thoroughly covers SDOH, with a focus on the primary determinants that may impact health status: income, housing, food security, education, etc. Given appropriate data about patients, perhaps AI can be useful in correlating information around these issues, enabling targeted, appropriate interventions where needed and providing assistance with accurate allocation of resources.
AI can also provide data-driven insights to consider structural inequities, with an eye toward remedies for healthcare delivery and health insurance coverage concerns.
Remember, however, that although AI will be capable of providing necessary insight, it cannot remedy these problems. That will be left to human intervention at administrative and policy-making levels, which is often fraught with controversy and economic concerns.
The Bottom Line
The bottom line is that for AI to be useful as a remedy based on the examples mentioned in this article, it must be trained to be equity focused, ethical, and comprehensive. It must take into consideration all types of populations in the United States with SDOH at its core.
This means that although utopia may not be an appropriate way to categorize AI healthcare applications, it definitely can make a positive contribution. Furthermore, considering AI for surgical procedures in medicine, using robotics inclusive of AI, there is no doubt that big gains are being made. Surgeons are performing operations with the help of AI robots, with precision that is astounding. But that’s another story with its own inherent concerns.
For further insight about AI and health equity, review Chapter 14 of Achieving Health Equity: Context Controversies and Remedies. This chapter, titled “Artificial Intelligence: A Problem or Remedy for Healthcare?”, defines terms, provides examples of AI use, and explores ethical issues and concerns and more.
Patti R. Rose, MPH, EdD, is a well-known author and public health educator who is the president and founder of Rose Consulting. She earned a master's degree in health services administration from the Yale University School of Public Health and a doctorate in health education from Columbia University Teachers College. She is the author of several books, the most recent of which is Achieving Health Equity: Context, Controversies and Remedies (Jones & Bartlett Learning, 2026). This book is a revision of Health Equity, Diversity, and Inclusion: Context, Controversies, and Solutions (Jones & Bartlett Learning, 2021).