Great post. I’m a clinical-translational lymphoma researcher and all of the issues you describe are a critical issue in moving our field forward.
I share your optimism that ML will be able to help us find features of cancer that humans would never be able to discover due to the sheer amount of data. In the past few years we have developed ML methods to decipher different subcategories of diffuse large B cell lymphoma (DLBCL, the most common lymphoma) using genomic and multi-omic strategies. We now have several competing systems of categorizing by both...
I agree that it would be nice to understand the mechanisms, but I actually think that is secondary if we have a tool that can helping patients now and we can understand the mechanisms later. If I feed H&E slides into a black box AI agent and the output it spits out inproves my patient’s survival, it helps them right now, today. Yes, I think understanding mechanisms underlying cancer biology is important (I have literally dedicated my life to it), but that can come after. A lot of cancer drug development has gone this way. One good example: thalidomide ... (read more)