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by kevinalexbrown
2701 days ago
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Dr Khullar suggests that AI will exacerbate biases in medical practice. His fundamental concern is that machine learning will codify biases and become self-fulfilling prophesies. But there is scant evidence that AI will worsen these disparities. If anything, a machine-learning point of view better addresses his concerns than a traditional one, because they can be much more quickly updated to correct for identified biases. Doctors spend years and years of hard work becoming efficient and effective human algorithms themselves, and updating those human algorithms in the face of newer evidence is difficult. In standard practice, biases are often invisible and uncodified to begin with. "Moral intuition" is something all doctors use, but it's also something of a black box in nearly every real-world use case. |
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1. there is already a major bias in medical diagnosis - a bias favoring those who can actually pay
2. automating even parts of the diagnostic process saves money and reduces cost, that is a huge benefit to everyone
3. not everything gets done immediately. lets figure out the basics first (getting classifiers working on whatever dataset we have) and then focus on getting it to work on everything. It isnt like medicine was right from day one...heck, I seem to recall leeches and bloodletting being the norm for a long time.
4. Almost every doctor i spoke to was afraid of ML/AI because it pierced their forced scarcity and threatened their wages. I might argue that Health Disparities are worsened currently because medical boards throttle residency programs and fellowships to create an artificially constrained supply and hence high prices. (before I get the rot response of...of course doctors will never go away...:yes, they wont go away, but they will focus less on rote things and increase throughput thus increase supply thus decrease wages.
5. We got all our training data from minorities. Incidentally, foreign countries are a lot more generous with training data. For our ML diagnostic firm, we had envisioned giving the product away for free in poorer countries where we could just get training data.