How a diverse dataset
can help close the
racial pain gap

The motivation behind the study was this mysterious pain gap

Pierson said that the basic idea was to train a machine learning algorithm to find any additional signals in the knee X-ray which isn’t being captured by regular risk scores and medical assessments, seeing if this algorithmic approach could narrow the racial pain gap for knee osteoarthritis and, subsequently, for other medical problems.

“The current standard system… was developed more than 50 years ago in white populations.”

And the KLG system is just one example of a diagnostic test that fails patients of color. For instance, there’s a kidney test that automatically adjusts scores for Black patients based on a discredited scientific theory on race and genetic differences. Because of this unfounded basis for adjusting diagnostic algorithms, nonwhite patients were more inclined to miss out on vital treatments. This system is still prevalent.

Machine learning and healthcare go pretty far back

The concept of machine learning in the diagnostic process is hardly new — a study for “an algorithm to assist in the selection of the most probable diagnosis of a given patient” was published in the National Library of Medicine in 1986. But neglecting to reexamine decades-old medical systems — especially ones that were built on the foundation of discredited racially-biased theories or on a foundation that fails to include nonwhite communities at all — is perpetuating healthcare inequality and harming vulnerable communities.

“Machine learning models trained on diverse datasets were better at predicting pain and narrowing the racial and socioeconomic pain gap.”

“It’s quite clear empirically that diversity of training set is important,” she said, adding that, when it comes to medicine in the broader context, “you shouldn’t throw all the women out of the study or only do your analysis on white European ancestry.”

Using machine learning to reduce (rather than perpetuate) bias in healthcare settings

Machine learning systems have a harmful and biased track record when it comes to diversity and inclusivity. Because these systems, until recently, were largely trained on predominantly white datasets, their outputs were at best skewed to certain demographics. At worst, they are racist and perpetuate discrimination.

“Using the algorithmic predictor that was trained on the diverse dataset, more black patients would be eligible for knee surgery.”

The researchers don’t see an algorithmic approach as a replacement for humans. Instead, it can be used as a decision aid. So rather than just a human or an algorithm making the final call, the radiologist can look at both the X-ray and the results from the algorithm, to see if they might have missed something.

An unconventional approach to pain or the new standard?

This approach is a bit more unconventional in that it wasn’t trained to do what the doctor does. It was trained to see what doctors and existing systems are missing. Rather than learn from the doctor, the algorithm was learning from the patient. When clinical knowledge is incomplete or inaccurate, you can go beyond the systems in play and learn from the patient directly.

“Rather than learn from the doctor, the algorithm was learning from the patient.”

What’s also an important takeaway from this research is that algorithms can be used for pure knowledge discovery — by training an algorithm to read thousands of X-rays, they were able to equate certain parts of the image to pain, detections that radiologists missed. Though because of the black box nature of algorithms, it’s unclear exactly what the algorithm is “seeing” — but it’s a notion that can be applied to other medical practices with archaic foundations that might not capture the lived experiences of the diverse demographic of patients.

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