Machine Learning is awesome for medicine, when they run your genetic sequence and then say “we should check for this weird genetic illness that very few people have because it’s likely you’ll have it” that comes from Machine Learning algorithms finding patterns in the old patient data we feed it.
Machine Learning is great for finding discrepancies in big data sets, like statistics of illnesses.
Machine Learning (AI) is incapable of making good decisions based on that statistical analysis though, which is why it’s still a horrible idea to totally automate medicine.
It also makes tons of mistakes and false-positives.
There’s a right way to use it, and the wrong way is by using proprietary algorithms that haven’t published openly and reviewed by the government and experts. And with failsafes to override the decisions made by the algorithms, in recognition that they often make terrible mistakes that disproportionately harm minorities.
Machine Learning is awesome for medicine, when they run your genetic sequence and then say “we should check for this weird genetic illness that very few people have because it’s likely you’ll have it” that comes from Machine Learning algorithms finding patterns in the old patient data we feed it.
Machine Learning is great for finding discrepancies in big data sets, like statistics of illnesses.
Machine Learning (AI) is incapable of making good decisions based on that statistical analysis though, which is why it’s still a horrible idea to totally automate medicine.
It also makes tons of mistakes and false-positives.
There’s a right way to use it, and the wrong way is by using proprietary algorithms that haven’t published openly and reviewed by the government and experts. And with failsafes to override the decisions made by the algorithms, in recognition that they often make terrible mistakes that disproportionately harm minorities.