Racial disparities in healthcare have been well documented in the United States, with Black and Brown communities experiencing worse health outcomes and receiving lower quality care than their white counterparts.
One study found that Black patients are far more likely to die from preventable causes than white patients.
One way in which Artificial Intelligence (AI) can help address racial disparities in healthcare is by reducing bias in medical decision-making. Studies have shown that healthcare providers, like any other humans, can be biased, consciously or unconsciously, in how they treat and diagnose patients.
For example, a study published in the Journal of the American Medical Association found that Black patients were less likely to receive pain medication than white patients, even when experiencing similar pain levels.
AI algorithms, on the other hand, are not biased in the same way that humans are. If an AI system is trained on a diverse and representative dataset, it is less likely to exhibit racial bias in its decision-making.
These advanced systems help triage patients or make diagnoses and could be trained on a dataset that includes a diverse range of patients rather than just one group. This would help ensure that the AI system makes decisions based on objective criteria rather than subjective biases.
AI can also be used to identify and address systemic inequalities in healthcare. For example, an AI system could analyze patient data to identify trends and patterns that may not immediately appear to human analysts.
This could include identifying areas with a higher prevalence of specific diseases or conditions among certain racial or ethnic groups or identifying regions with a higher prevalence of certain types of treatment.
By identifying these trends and patterns, AI can help healthcare providers target their resources more effectively and address underlying inequalities in the healthcare system.
The use of artificial intelligence in the healthcare industry has the potential to significantly improve patient care and reduce disparities in care.
In particular, AI can help address racial, gender, age, and income disparity in medical treatment and health insurance coverage.
One way in which AI can lower racial disparities in medical care is through the use of natural language processing (NLP) to analyze electronic medical records (EMRs).
By analyzing the language used in EMRs, AI can identify patterns and biases that may contribute to unequal treatment of patients based on race.
In addition to analyzing EMRs, AI can also be used to develop personalized treatment plans for patients based on their needs and characteristics.
These algorithms can analyze a patient’s medical history, genetic data, and other relevant information to determine the most effective treatment options.
This personalized approach to treatment can drastically increase the quality of patient treatment because precision related to the individual is used instead of general studies currently implemented for care plans.
Another way in which AI can help reduce disparities in healthcare is through the use of predictive analytics.
By analyzing large datasets of patient data, AI can identify trends and patterns that may contribute to unequal access to care or treatment.
There are many communities that are unwilling or skeptical of medical care due to the poor outcomes in the past from public experiments that treated them with far less respect than they deserved.
AI can also be used to improve access to healthcare for underserved populations. This has been proven in recent years by the increased use of telemedicine technologies powered by AI.
These can help connect patients in remote or underserved areas with healthcare providers, allowing them to receive the care they need without traveling long distances. This also reduces the time and money being spent on patient care, which is sure to make most insurance companies smile.
Perhaps the most significant benefit of Artificial Intelligence/Machine Learning (AI/ML) systems is big data. Imagine a world where we emphasize data collection on health issues ranging from virus outbreaks to gunshot victims.
The information we could gain from this data collection could revolutionize our legal system. Suddenly new insight could drive innovative thinking in how we treat communities that may have been disparaged in the past.
The point is we need new ways to unearth connections and ideas that haven’t been considered before.
While there are endless talking heads willing to discuss the moral merits of racial, gender, or other types of inequalities, there are not as many with loud microphones offering solutions.
With AI, these solutions are unbiased and look purely at the data. They can allow us to glean incredible insight that could transform our thinking about the future.
The key to having a more accepting world where a black pregnant woman is treated with the same professionalism, care, and empathy as a high-paying white man rests on leveraging technology in new ways.
The other thing to consider is this movement is already a reality. There was an exceptional study that assessed the use of AI/ML products in healthcare recently by the Royal College of Physicians.
This is a great place to start, as it introduces talking points for discussing how to apply these ideals.
Whatever we as a society decide in the future, we need to start thinking outside the box. We don’t want to leave anyone behind, especially when we have the medical advancements to solve so many of our common issues as human beings.
Written by: Emmanuel J. Osemota
