| We are working in the breast cancer space now looking at breast cancer and ultrasound (not just from a screening / diagnostic perspective - also treatment planning for medical oncologists and treatment response planning). We don't use deep learning - we use Biophysical models. We hate using the term "AI". This is a very challenging discipline to explain to VCs. Also, speaking to point 2 here - the "value" of building tools for ultrasound is often dismissed by VCs because "ultrasound isn't used for screening or diagnosis". This is an insane perspective from our position when we are practically based within hospitals, collaborating strongly with radiologists and medical oncologists who work with ultrasound on a daily basis. We are very embedded within the hospital and look to understand the clinicians workflow and decision making processes first, as well as understanding what's possible given the hurdles involved in data access (which can still be tricky even when you are through IRBs and ethics). We have found that telling VCs the reality about working with hospitals and doctors can often limit their excitement about your company prospects. Our success to date has largely been as a result of doctors and hospitals who believe in us, see the value in what we are doing. They have put time and effort into collaborating because they are impressed with what we have been able to do results wise by bootstrapping as a small team, rather than as a VC funded shiny startup. In a weird way i would say that at it's best times medtech can be one of the "purest" industries to work in. By this i mean ultimately your technology works or it doesnt (at least from the medical communities perspective - again VCs are a different story). There are obviously exceptions to this (Theranos anyone) and there are issues around the 510K process but on the whole there is a big price to pay for making unsubstantiated claims (say compared to aspirational lifestyle marketing). |