A study co-authored by data scientists at Rutgers-Newark University finds that the AI algorithms increasingly used to treat and diagnose patients are free from bias and bias that can hinder healthcare for Black and Latinx patients. There may be blind spots.
Faye Cobb-Payton, a professor of mathematics and computer science, explains how AI techniques and algorithms rely on data that can lead to generalizations about patients of color and their cultural backgrounds. We are researching whether or not it is incorporated into the daily living environment.
Peyton, who is special advisor to the chancellor on inclusive innovation at Rutgers-Newark University, recently co-authored a study on AI and health care inequalities in Milbank Quarterly, which examines population health and health policy. did. Additional authors are Thelma C. Hurd of the Institute for Health Disparities, Equity, and Exposomes at Meharry Medical College and Darryl B. Hood of The Ohio State University School of Public Health.
Peyton is co-founder of Rutgers University's Institute for Data, Research, and Innovation Sciences (IDRIS), which integrates interdisciplinary research in the fields of medicine, public health, business, cultural studies, and technology. Part of its mission is to find the best ways data can be used to serve the community and illuminate the intersections of data, technology, and society across a variety of sectors.
The study Peyton co-authored found that AI developers are under-represented and Black and brown patients are under-represented in medical research, leading to algorithms perpetuating false assumptions and the nuances that more diverse fields can offer. It turns out that some nuances may be missing. Developer and patient data. Healthcare providers can also play an important role in ensuring treatment goes beyond algorithms.
“How is the data entered into the system, and does it reflect the population we are trying to serve?” Payton asked. “It's also about having a human, like a provider, doing the interpreting. Have you made sure there's a human in the loop at all times? There has to be some form of human intervention throughout.”
Algorithms rely on “big data” such as medical records, images, and biomarker values. But the study says it doesn't incorporate “small data” such as social determinants of health, such as access to transportation, healthy eating, and patients' communities and work schedules. This can make it more difficult for patients to follow treatment plans that require frequent doctor visits, physical activity, and other measures.
“The cost of fresh food is not taken into account. This may not account for the fact that people without transportation are working two jobs. They do everything their doctor says. They may be trying to comply, but no one is talking to them about why, so people think they're not complying,” Payton said.
“This may create a 'metaphorical' characterization of Black patients and influence patients' perceptions of the trustworthiness of the health care system,” according to the study.
“The algorithm could suggest a treatment plan,” Payton said. “It may indicate what resources to use to treat a patient, but those recommendations may not take into account where the patient lives, works, or plays. There is.”
Without socio-economic considerations based on the patient's daily life, treatment can be compromised. However, the results could be improved with more information. For example, doctors may prescribe long-acting drugs or interventions that don't require travel, Payton says.
Algorithmic bias may also fail to explain disparities in medical outcomes, such as the nearly 30 percent higher overall mortality rate for non-Hispanic black patients and non-Hispanic white patients, and this number may be due to higher mortality rates for certain patients. This is also thought to be due to. disease.
“In general, they experience more cases of heart disease, stroke, and diabetes. Black women have more severe breast cancer,” Payton said. “This is what the data shows, but preliminary research shows that algorithms may be racially biased, even if Black patients are more severely ill than other populations.” It can lead to misdiagnosis, access to appropriate resources, or delays in treatment.”
A further concern is that the algorithm does not take patient location into account.
“Most of the patient data in the United States comes from three states: California, Massachusetts, and New York.” That can impact the delivery of care and, in some cases, the quality of care,” Payton said.
One solution, according to research by Peyton and colleagues, is to increase diversity not only among technology developers but also among physicians.
According to sources cited in the study, only 5 percent of practicing doctors in 2018 identified as Black, and about 6 percent identified as Hispanic or Latino. The percentage of underrepresented developers is even lower.
“It is important to understand the biases that exist among traditional education and health care delivery professionals,” Payton says. “It is important for developers to have domain and technical skills to gain a deeper understanding of healthcare (and the problem domain).”
Peyton et al.'s research also calls for more rigorous processes for reviewing and evaluating data fed to algorithms to ensure it's not exposed to biases that could exacerbate health care disparities.
With the right safeguards in place, AI could be even more likely to help patients, Payton said.
The study provides a list of recommendations that can reduce potential harms, including collaborative actions among health stakeholders, developers, end users, and policy makers across the AI lifecycle. It is already making a difference in the lives of some patients.
“Predictive analytics can analyze large datasets for patterns and risk factors associated with a disease. AI can help medical professionals analyze images for disease conditions. Text, images, audio , Generative AI (GenAI) using event video can inform patient monitoring,” Payton said. “Despite bias, privacy, security, and several other concerns, AI has the potential to have positive effects.”
