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New York/Canadian-Media: A new study shows how training deep-learning models on patient outcomes could help reveal gaps in existing medical knowledge, Karen Hao, the senior AI reporter at MIT Technology Review reported.
Image: Measuring pain scale. Image credit: MIT Technology
In the last few years, research has shown that deep learning can match expert-level performance in medical imaging tasks like early cancer detection and eye disease diagnosis. But there’s also cause for caution. Other research has shown that deep learning has a tendency to perpetuate discrimination. With a health-care system already riddled with disparities, sloppy applications of deep learning could make that worse.
Now a new paper published in Nature Medicine is proposing a way to develop medical algorithms that might help reverse, rather than exacerbate, existing inequality. The key, says Ziad Obermeyer, an associate professor at UC Berkeley who oversaw the research, is to stop training algorithms to match human expert performance.
The paper looks at a specific clinical example of the disparities that exist in the treatment of knee osteoarthritis, an ailment which causes chronic pain. Assessing the severity of that pain helps doctors prescribe the right treatment, including physical therapy, medication, or surgery. This is traditionally done by a radiologist reviewing an x-ray of the knee and scoring the patient’s pain on the Kellgren–Lawrence grade (KLG), which calculates pain levels based on the presence of different radiographic features, like the degree of missing cartilage or structural damage.
But data collected by the National Institute of Health found that doctors using this method systematically score Black patients’ pain as far as far less severe than what they say they’re experiencing. Patients self-report their pain levels using a survey that asks how much it hurts to do various things, such as fully straightening their knee. But these self-reported pain levels are ignored in favor of the radiologist’s KLG score when prescribing treatment. In other words, Black patients who show the same amount of missing cartilage as white patients self-report higher levels of pain.
This has consistently miffed medical experts. One hypothesis is that Black patients could be reporting higher levels of pain in order to get doctors to treat them more seriously. But there’s an alternative explanation. The KLG methodology itself could be biased. It was developed several decades ago with white British populations. Some medical experts argue that the list of radiographic markers it tells clinicians to look for may not include all the possible physical sources of pain within a more diverse population. Put another way, there may be radiographic indicators of pain that appear more commonly in Black people that simply aren’t part of the KLG rubric.