Artificial intelligence (AI) has been at the forefront of many fields, including health care. Where inequities remain a pressing challenge, access to timely diagnosis and preventative care is often limited. These gaps can lead to problems like late-stage detection of diseases, poorer health outcomes, and higher treatment costs.

Advancements in AI are changing the way early detection and prevention are viewed and used. From identifying patterns in data to enhanced imaging, AI provides efficient ways to bridge gaps in health care by improving disease detection and prevention in underserved communities.

How AI Can Make an Impact in Underserved Communities

Screening and Disease Prevention

Racial and ethnic minorities often don’t get the health care screenings they need, which can help find diseases early when treatment is easier.

For example, over half of Black people in the US have high blood pressure, making it among the highest rate in the world. Black people also have higher rates of severe high blood pressure, which may be a result of limited access to health care. In rural areas, residents also face challenges accessing care due to lack of health insurance coverage and nearby care.

AI screening tools have the power to upend these disparities. Wearable devices and remote monitoring systems can collect and analyze patient data – like blood pressure, heart rate, and oxygen levels – in real time. When a problem is detected, these tools can automatically alert care teams, supporting timely interventions.

Medical Imaging

In the US, people of color are more likely to be diagnosed with cancer at a later stage than white people. In many cancers, the stage of diagnosis is a key predictor of survival.

Imaging tests are one of the best ways to diagnose diseases as early as possible. AI improves the efficiency and accuracy of imaging diagnostics. From X-rays to MRIs to CT scans, AI tools can find the subtlest of abnormalities that suggest the presence of a disease. By helping radiologists and other health care professionals interpret results, AI supports healthcare facilities in making more efficient, accurate diagnoses for all patients.

Health Care Data Analysis

In health care, data abounds. But large datasets require time and resources to navigate – something many communities lack. With AI, machine learning algorithms can analyze data like electronic health records (EHRs) and genetic information. This can help to predict outcomes and improve patient care.

For instance, in some cancers, racial genetic differences can raise a person’s risk of getting and dying from the disease. AI-driven tools can use genetic factors and other data to detect high-risk patients who may benefit from earlier screenings.

Clinical Support

In many underserved communities, staffing is short and patient needs are high. For instance, rural and inner-city communities are more likely to experience nursing shortages. This is partly because they lack resources and funding.

AI can take on tasks like analyzing medical history and symptoms, interpreting test results, and aiding in decision-making. It can help with appointment scheduling, medication reminders, and patient follow-ups. It can even read a provider’s clinical notes, using natural language processing (NLP) to help with record-keeping and data retrieval.

By easing some of the burden on providers, AI tools can improve the efficiency of treating patients, helping them address the needs of more patients.

The Ethical Challenges of AI in Patient Care

Despite the promise of AI in health care, it’s not a perfect solution. Ethical issues are at the forefront of many health care professionals’ minds. This is especially critical among communities that have already been subject to a history of unequal care.

Data privacy is a primary concern. As AI tools sift through massive amounts of data, there must be oversights to ensure that data is used responsibly and patient data stays protected. Privacy regulations and security measures must also be strictly enforced, all while maintaining transparency to ensure public trust.

Bias is another key factor, especially as it pertains to underserved communities. Because AI systems reflect the data they are trained on, their analysis and recommendations may be biased. To prevent this, AI systems must be trained on diverse and representative datasets. These systems must also be continually monitored and assessed for bias.

AI Today for a Healthier Tomorrow

AI-powered tools hold immense promise for early disease detection and prevention, especially in underserved communities where health care access remains a challenge. By leveraging the capabilities of AI, we can identify health risks earlier, provide timely interventions, and improve outcomes for vulnerable populations.

However, collaboration among health care providers, policymakers, and technology innovators is essential to ensure that these tools are equitably deployed. Together, we can harness the power of AI to close the healthcare gap and build a future where everyone has access to lifesaving care.