Healthcare providers and managers use AI technologies to serve patients faster and better. AI tools help read medical data, check diseases, watch chronic conditions, and sometimes help with language problems. But these tools rely on the data they are built on. Recent studies show many AI systems do not work well for people from different cultural and ethnic groups.
In the U.S., patients come from many cultures with different beliefs, customs, and languages. AI that does not consider this can give wrong or less useful healthcare advice. For example, AI trained mostly on data from men made mistakes diagnosing heart disease in women at a rate of 47.3%, but only 3.9% for men. Another example is AI that checks skin problems—it made about 12.3% more mistakes on darker skin than lighter skin.
Bias and errors in AI systems can make health worse for minority groups. AI diagnosis is about 17% less accurate for minorities in primary care. This helps keep the healthcare gap in America.
To fix these problems, AI tools must be made with attention to culture, language, and local health needs. This is where working with the community is very important.
Community engagement means working directly with patients, local leaders, and cultural groups when designing, testing, and using AI tools. Still, only about 15% of healthcare AI tools are developed with this kind of teamwork.
Medical managers and IT teams in the U.S. often find it hard to bring new technology to all kinds of patients. Working with communities helps to:
As U.S. healthcare tries to be fairer, including community views is not optional but needed for AI to work well.
The U.S. still has a digital divide. About 29% of adults in rural areas lack access to AI health tools because they do not have good internet or skills to use digital devices. This makes it hard for some groups to benefit from telemedicine and AI alerts.
Telemedicine using AI has cut care time in rural areas by 40%. This shows AI can cross distance problems if it is easy to use. Without community input, systems may not fit local tech or preferences. Community engagement helps find out:
For example, some AI telemedicine platforms in areas with many languages work better by adding language options and familiar ways to talk.
When community members take part, healthcare leaders and IT staff can build AI tools that solve tech problems, making these tools easier to use and improving health in rural areas.
Making culturally competent AI means building tools that respect and adjust to cultural differences like health beliefs, treatment choices, and language. Researchers Nivisha Parag, Rowen Govender, and Saadiya Bibi Ally suggest key points such as:
Healthcare leaders in the U.S. can use these ideas to check AI tools before adding them to daily work. Ignoring these can make health gaps worse, like giving wrong tests or treatments.
Doctors must also be careful not to depend too much on AI. If not watched closely, AI might lower doctors’ careful decisions or leave out vulnerable groups.
One area where AI and community work come together is phone automation in medical offices. Some companies build AI phone systems to help patient communication, lower staff work, and improve patient experience.
These systems can:
But building these without patient input risks making messages that do not fit the culture or confuse people, especially those who do not speak English well or older patients who are not used to machines.
Community feedback helps make sure:
By working with medical leaders and patients during development, companies can make AI phone systems easier to use and more trusted. This leads to better patient involvement and satisfaction.
Also, AI automation helps healthcare teams by letting staff spend more time caring for patients instead of doing routine work. When community views guide automation, it fits real needs and respects patient diversity.
Many AI healthcare tools show good early results. Still, 85% of studies last less than 12 months when looking at health fairness. For AI to reduce gaps fully in the U.S., medical leaders and IT managers need to support longer tracking that includes different patient groups.
Policymakers should create rules that support responsible AI building, use, and monitoring. These rules must require community involvement during AI development.
AI in U.S. healthcare offers many chances to improve diagnosis, personal care, and access. But it works best when it fits the culture and includes all groups. Working with communities during AI tool development helps AI fit different people’s needs, cuts bias, and raises trust.
Healthcare managers, practice owners, and IT teams must work closely with the communities they serve. This is especially true for front-office AI tools like phone automation, which can make patient communication and work flow better.
By building AI tools with help from communities, healthcare groups can serve all patients better—including minorities, immigrants, rural residents, and those who do not speak English—and improve how healthcare runs in a more digital world.
AI enhances diagnostic capabilities, improves access to care, and enables personalized interventions, helping reduce health disparities by providing timely and accurate medical assessments, especially in underserved populations.
Prominent AI applications include risk stratification algorithms that better control hypertension, telemedicine platforms reducing geographic barriers, and natural language processing tools aiding non-native speakers, collectively improving health management and access.
Significant challenges include algorithmic bias leading to diagnostic inaccuracies, the digital divide excluding rural and vulnerable populations, insufficient representation in training datasets, and lack of community engagement in AI development.
Algorithmic bias results in about 17% lower diagnostic accuracy for minority patients, perpetuating healthcare disparities by providing less reliable AI-driven assessments for these groups.
The digital divide excludes approximately 29% of rural adults from benefiting from AI-enhanced healthcare tools, limiting the reach of technological advancements and widening health inequities in rural settings.
Only 15% of AI healthcare tools include community engagement, but involving affected populations is critical for ensuring that AI solutions are relevant, culturally appropriate, and more likely to be adopted effectively.
Future research should focus on equity-centered AI development, longitudinal outcome studies across diverse populations, robust bias mitigation, digital literacy programs, and creating policy frameworks to ensure responsible AI deployment.
Potential risks include overdiagnosis, erosion of clinical judgment by healthcare providers, and inadvertent exclusion of vulnerable populations, which might exacerbate rather than reduce existing health disparities.
Telemedicine platforms have been shown to reduce time to appropriate care by 40% in rural communities, effectively overcoming geographic barriers and improving timely healthcare access.
The review followed PRISMA-ScR guidelines, systematically identifying, selecting, and synthesizing 89 studies from seven databases dated 2020-2024, with 52 studies providing high-quality data for evidence synthesis.
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