Healthcare in the United States serves many people from different ethnicities, languages, cultures, and economic backgrounds. AI tools that work well in one place might not work well in others if they do not think about the different needs and cultural traditions of patients.
Research shows AI systems often do not work the same for all groups. For example, AI tools for diagnosing heart disease make errors almost half the time for women, but only about 4% of the time for men. Similarly, AI has more mistakes—about 12% more—when diagnosing skin problems in people with darker skin compared to those with lighter skin. These problems happen because many AI programs use data that do not include enough examples from all groups, which can cause unfair results and may make health differences worse.
Cultural differences also change how patients understand symptoms, follow medical advice, and stick to treatments. Many AI tools use general methods that miss these cultural details, so they do not work as well. For example, an AI app for diabetes that was designed for indigenous groups gave advice about food based on their culture and even included traditional healing. This helped patients follow the advice better but also raised questions about privacy and trust. These issues must be handled with open talks and consent that respect cultural rules.
In the U.S., immigrant and minority groups often face challenges because of language and sometimes distrust healthcare AI. Good AI tools need to support many languages and explain how they make decisions clearly. This helps patients trust their doctors and get better health results.
Community engagement means working closely with the people who will use the AI tools. This is important to gather real information that helps design and use AI better. For healthcare leaders and IT managers, knowing the community is key to picking or creating AI tools that fit their patients.
At the George Washington University School of Medicine and Health Sciences, along with the University of Maryland Eastern Shore, they started a project called “AI-FOR-U.” This project creates AI to help reduce health gaps in less wealthy areas of Washington D.C., Maryland, and Virginia. They got a large grant from the National Institutes of Health and included community partners like Unity Healthcare and local schools to make sure the AI meets real needs.
The project uses focus groups, interviews, and surveys to collect ideas from different communities, including Black, Latino, LGBTQ+, and low-income groups. They work on making AI fair and easy to understand in areas like heart and metabolic diseases, cancer, and mental health. The project builds trust and deals with worries that AI might be unfair or give wrong answers if it uses data that does not represent everyone.
This model shows a good way for medical practices to involve local groups and patients when using AI. It helps make sure the technology fits with cultural values and what patients expect. It also promotes clear communication and helps patients and health workers accept AI tools better.
One main reason to involve the community in healthcare AI is to stop bias. Bias happens when the data used to teach AI does not cover all kinds of people or ignores cultural and biological differences.
Bias can be very harmful. For example, if AI tools do not spot disease risks or symptoms correctly in minority groups, patients might get wrong or less care. To prevent this, AI systems need regular checks for bias, and data should be diverse and include many types of people.
Researchers at George Washington University work on making AI fairer and clearer. This means AI tools not only give correct results but also explain why they gave those results. This helps doctors and patients trust the AI more.
Medical leaders should ask AI providers if their tools were trained on data that include different groups. They should also find out how fairness is watched over time.
Cultural competence means making healthcare tools that respect the values, beliefs, and habits of different patient groups. In AI, this means building programs, language tools, and experiences that fit cultural backgrounds.
For example, AI translators help healthcare workers talk better with patients who speak little English. Still, people must check these translations to avoid mistakes with tricky medical words.
Also, AI used in personalized medicine should think about genetic differences and cultural preferences when planning treatments. For instance, pharmacogenomics changes medicine based on a person’s genes, helping reduce bad drug reactions and making it easier for patients to take their medicine properly.
In places like South Africa, AI has to handle many official languages plus sign language and respect traditional medicine. Japan uses AI to care for its older population in ways that fit their culture.
In the U.S., many cultural and immigrant groups use healthcare every day. AI tools that respect culture can improve how happy patients are and their health results. This needs ongoing input from communities, training for healthcare workers, and ethical rules that protect cultural views on privacy and decisions.
AI is not only for finding diseases or planning treatments. It also helps with routine tasks that support patient care. For medical managers and IT staff, AI automation in front-office work can cut work, improve accuracy, and make things better for patients.
Some companies like Simbo AI focus on AI phone services and front-office tasks for healthcare. Their systems use advanced language skills to handle appointment bookings, patient questions, prescription refills, and insurance checks without adding more work to staff.
Using AI on the phone reduces wait times, lowers missed calls, and gives more steady service. It also helps medical offices follow rules about privacy and billing by capturing data correctly and keeping communication safe.
AI tools also help staff by studying communication patterns, guessing busy call times, and supporting multiple languages. This is very important in culturally mixed places in the U.S. where language can stop people from getting care.
Adding AI to daily work helps healthcare teams focus more on patient care, raises patient satisfaction, and cuts costs. IT managers should pick AI systems that easily connect with electronic health records and practice management software already used in the office.
Trust matters a lot when bringing AI into healthcare. Patients and doctors need to be sure AI decisions are correct, fair, and respect privacy. Trust grows from open talks, patient involvement in making AI, and clear facts about how data is used.
The NIH-supported AIM-AHEAD program supports these ideas by putting community involvement in every part of AI building. They make sure consent forms are easy to understand in many languages and match cultural ways. This helps patients see how their data will be used and why AI can help their care.
Healthcare leaders should provide easy explanations about AI for patients and workers. They should also talk often with community members to get feedback on how AI works and fits culture.
It is also key to report openly on how accurate AI is and check for bias regularly. This keeps AI working well as communities change.
AI can help improve how healthcare connects with communities. AI systems can look at large amounts of community feedback from social media, surveys, and patient talks to find problems, health trends, and areas that need attention.
Tools like Social Pinpoint use AI to quickly analyze community data and give healthcare leaders useful findings. AI translation tools that work with many languages help break communication barriers across different patient groups.
Other AI products such as FranklyAI engage users in conversations based on community needs, making outreach more personal and interactive.
Healthcare leaders can use these AI tools to improve outreach, hear from many voices, and design services that better meet local health needs.
Healthcare leaders, owners, and IT staff in the United States should pay close attention to culture and community when using AI. By using AI tools that are fair, clear, and include everyone, they can improve patient care and satisfaction.
They should focus on front-office AI automation like Simbo AI to make operations smoother and easier for patients to access. Making sure AI is fair for all groups means working closely with communities and checking for bias often.
Using these ideas together allows healthcare providers to use AI well while supporting fair and culturally respectful care for America’s diverse populations.
The AI-FOR-U project aims to develop trustworthy AI tools to address health disparities in under-resourced communities, enhancing fairness and explaining risk-prediction models in healthcare.
The project is a collaboration between the George Washington University (GW) School of Medicine and Health Sciences and the University of Maryland Eastern Shore (UMES).
Qing Zeng, PhD, a professor of clinical research and leadership and director of GW’s Biomedical Informatics Center, is leading the project.
The AI tools will focus on cardiometabolic disease, oncology, and behavioral health, as selected by community partners.
The impact will be evaluated through clinical use cases and by measuring frontline workers’ trust in the AI tools.
The project received a two-year grant of $839,000 under a larger $1.9 million initiative to advance health equity using AI.
Community partners include various organizations serving diverse populations, such as Alexandria City Public Schools and Unity Healthcare.
The project aims to ensure that AI applications do not increase healthcare inequities and improve users’ understanding of AI decision-making.
Community engagement is integral, with input from partners during focus groups, interviews, and surveys to guide tool development.
The project is part of the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD), supported by the NIH.
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