Artificial intelligence has huge potential for many healthcare challenges – but there are still many hurdles that must be overcome.
At a keynote for the Association for Computing Machinery Conference on Health, Inference and Learning this past week, Dr. Alan Karthikesalingam, research lead at Google Health UK, described three myths commonly encountered in the path to building and translating AI models in clinical settings.
When it comes to implementing deep learning technology, he asked: "Why is there a gap between expectations and reality?"
Here are three common misconceptions Karthikesalingam said must be addressed.
1. More data is all you need for a better model.