As someone whose intention is to go to Medical School and pick up programming (+ math skills) to potentially work at the intersection of ML + Healthcare, the knowledge of the regulatory hurdles expressed is discouraging. Not sure if it really is worth the effort to study tech on top of medicine. Are there any people with experience within ML + Healthcare/Medicine or know of startups that are making great strides within this realm?
I used to work in the healthcare vertical. While there are regulatory hurdles, they are there for good reason. Move fast and break things does not work in this industry and will get you fired.
You will have better luck working with one of the larger companies who have a good history with the FDA, and more importantly, have good relationships with hospitals and physicians. They are aware of the time and resources it takes to push something out. Pay will not be FAANG level or anywhere close, but they usually have great work culture and WLB.
Thanks for the comment. Do you have any feedback for a future physician interested within the intersection of ML + medicine (does not strictly need to be "healthcare")? Would learning ML and all the other things needed to know be helpful (huge time investment needed here)? Ultimately, would be interested in leveraging medical knowledge into a startup capacity.
As a traditional physician, I imagine your value add would still be your domain expertise. The physicians I worked with were purely consumers of AI tech and had a say in the design of the software. But AI and more generally software is becoming an important part of medicine so I guess it couldn't hurt to learn some basic ML.
When I was an MD/PhD student in 2017-2018, there were only a handful of labs specializing in applied medical ML and computational biology. Since leaving to work as an ML eng/backend eng, I have been surprised by how the relationships between academics, practitioners, and investors in pure software contribute to a positively reinforcing loop. On the medicine side, the academics lean towards software skepticism, the investors make fewer and safer bets to compensate for historically lower margins/growth/return multiples compared to pure software, and the subset of practitioners get payed orders of magnitude less and have correspondingly less engineering development. The differences affect everything from managerial quality, skill and career development, location flexibility, upward mobility, product scope and impact.
