this post was submitted on 21 Nov 2023
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It does, but it also has a black box problem.
A machine learning algorithm tells you that your patient has a 95% chance of developing skin cancer on his back within the next 2 years. Ok, cool, now what? What, specifically, is telling the algorithm that? What is actionable today? Do we start oncological treatment? According to what, attacking what? Do we just ask the patient to aggressively avoid the sun and use liberal amounts of sun screen? Do we start a monthly screening, bi-monthly, yearly, for how long do we keep it up? Should we only focus on the part that shows high risk or everywhere? Should we use the ML every single time? What is the most efficient and effective use of the tech? We know it's accurate, but is it reliable?
There are a lot of moving parts to a general medical practice. And AI has to find a proper role that requires not just an abstract statistic from an ad-hoc study, but a systematic approach to healthcare. Right now, it doesn't have that because the AI model can't tell their handlers what it is seeing, what it means, and how it fits in the holistic view of human health. We can't just blindly trust it when there's human lives in the line.
As you can see, this seems to be relegating AI to a research role for the time being, and not on a diagnosing capacity yet.
You are correct, and this is a big reason for why "explainable AI" is becoming a bigger thing now.
There is a very complex algorithm for determining your risk of skin cancer: Take your age ... then add a percent symbol after it. That is the probability that you have skin cancer.