Something to like in Notes?
Inconceivable!
AI systems like Watson are examples of machine learning based on probabilistic neural networks. They digest training data and “fit” a function to them that accommodates far more parameters and potential treatment plans than a typical human doctor could remember or consider. They then sort their output based on the probability of a successful outcome. The training data is constantly being updated with the real world results as the algorithm learns. This isn’t spooky, its just another tool that is in its infancy and needs to be refined. The big problem with these systems is they are only as good as the training data and, as the article indicates, this can render the technology useless. The reason human doctors are still much better than Watson with a fraction of the compute power is because they visualize “causal models” based on real world experience rather than just using statistical methods based on static data.
lol...It was a different time.