Raw LLM Responses

as someone who works in the field (of AI), I think what's most startling about this kind of work is seemingly how unaware people are of both its prominence and utility. the beauty of something like malignant cancer (... fully cognizant of how that sounds; I mean "beauty" in the context of training artificial intelligence) is that if you have the disease, it's not self-limiting. the disease *will* progress, and, even if you "miss" the cancer in earlier stages, it'll show up eventually. as a result, assuming you have high-res photos/data on a vast number of patients, and that patient follow-up is reliable, you'll end up with a huge amount of radiographic *and* target data; i.e., you'll have all of the information you need from before, and you'll know whether or not the individual developed cancer. training any kind of model with data like this is almost trivial -- I wouldn't doubt it if a simple random forest produces pretty damn solid results ("solid" in this case is definitely subjective -- with cancer diagnoses, peoples' lives are on the line, so false negatives are highly, highly penalized). a lot of people here are spelling doom and gloom for radiologists, though I'm not quite sure I buy that -- I imagine what'll end up happening is a situation where data scientists work in collaboration with radiologists to improve diagnostic algorithms; the radiologists themselves will likely spend less time manually reviewing images and will instead focus on improving radiographic techniques and handling edge cases. though, if the cost of a false positive is low enough (i.e. patient follow-up, additional diagnostics; NOT chemotherapy and the like), it'd almost be ridiculous to not just treat all positives as true. the job market for radiologists will probably shrink, but these individuals are still highly trained and invaluable in treating patients, so they'll find work somehow!