<https://ja.coursera.org/lecture/stanford-statistics/extra-industry-insights-challenges-to-using-data-science-in-medicine-KesaW#lecture>
<http://schema.org/transcript>
"Traditional drug discovery is typically led by biologists. Right? And so, in a program like liver cancer that would, most typically, entail someone who has spent their entire life studying the disease, and maybe they come up with some piece of biology or some mechanism that has the potential to be modulated, right, to help people with that disease. That type of research, can take decades, a lot of basic science, a lot of cognitive thinking. We replace a lot of that work with data science. We collect tons of data, more data than any human could possibly ingest and interpret on their own, process that information and essentially come up with the same types of biological theories that you could apply. But all this happens in the course of a few minutes, rather than the course of a few decades. The challenges we have at work have very little to do with technology choices, and generating potential new drugs that have promise. More of our challenge sare on, it's an industry that has a lot of tradition behind it, of having that classic, lab coated scientists doing that work. And most of our work is helping to educate people that data science can actually make this difference, right? You don't need some prestigious professor who's been at it for 20 years, that kind of software can do the same thing, if not better. Like that concept, for people in this industry is a very tough one for them to deal with, and even just in the medical profession as a whole, there's been, I think a lot of talk about what is the role of the clinician, right? What's the role of your doctor moving forward? And does that become one where they're doing less on decision support and software is doing that, and it's more about sort of that personal care perspective. Medical doctors don't like that. They don't like that, and so, a lot of the work that we have to do is more on education, and getting people comfortable, and sitting down with prestigious scientists and telling them something that's a little out of their comfort zone. That's most of my challenges. Strangely enough, it's not writing code. It's not solving technical issues in that regard."
<https://ja.coursera.org/lecture/stanford-statistics/extra-industry-insights-challenges-to-using-data-science-in-medicine-KesaW>