Biases in Healthcare: AI Integration Raises Alarm for Patient Disparities
GE Healthcare unveils AI-enabled urology software. Johnson & Johnson Medtech has partnered with Nvidia to use AI to improve surgery. These are just a few of the stories related to Artificial Intelligence in healthcare. This flood of new products is causing concern for the World Health Organization(WHO).
The healthcare industry continues to confront challenges related to biases, both implicit and systemic, which can significantly impact patient outcomes and contribute to disparities in care. With the integration of artificial intelligence (AI) into healthcare systems, there is a growing concern that these biases may be exacerbated rather than alleviated. One particular area where biases have been highlighted is in the use of pulse oximeters, especially concerning their accuracy in black patients.
Research has found that pulse oximeters, which are used to measure oxygen levels in the blood, might not give accurate results in people with darker skin. This could mean that their oxygen levels are underestimated, which can be dangerous, especially during surgeries or emergencies. As a result, black patients might not get the right diagnosis or treatment, making existing healthcare inequalities worse.
Also, there are big differences in how well black and white patients do after surgery, showing that there are unfair biases in healthcare systems. For example, a study in the US found that black patients are nearly three and a half times more likely to die within a month after surgery. This shows how urgent it is to fix these unfair differences. They might happen because black people don't always get the same good healthcare, they face money problems, doctors might have unfair opinions, or healthcare workers might not understand their culture.
Using artificial intelligence (AI), especially big models like large language models (LMMs), in healthcare systems could make these biases even worse. LMMs learn from lots of data to make predictions, but if that data is biased, the models will be too. In healthcare, this might lead to computers making decisions that are unfair to certain groups of patients, making the differences in diagnosis, treatment, and outcomes even bigger.
To deal with these problems, we need to do many things. Healthcare places need to focus on making sure everyone gets treated fairly, no matter who they are. This could mean training healthcare workers to be fairer, making sure medical studies include people from different backgrounds, and testing AI systems to make sure they treat everyone the same.
Also, we need more honesty and responsibility in healthcare systems that use AI. Places that give healthcare need to watch closely how well AI systems work, check often for any unfairness, and keep making them better. And, the people who make the rules and laws about healthcare need to make sure AI is used in a way that's fair and right for everyone.
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Overall, AI could help make healthcare better for everyone, but we need to be careful. We have to work hard to fix unfair biases, make sure everyone is included, and make sure the rules and laws are fair too. Then, we can make sure everyone gets the same good healthcare.
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