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Algorithms have been competitive with humans for 20 years on radiology.

And yet humans have not yet been replaced.

Tell me what you expect to be different about the next 10 years that wasn't in the last 20? I'm open to being convinced. But you have to not just say that computers are going to be better - you have to explain why there wasn't already a switch.

Have you every done anything entrepreneurial in healthcare or just tried to introduce a new or different treatment standard?

1) It just takes longer in this field. Takes longer, but not necessarily more difficult (technically).

It's a nightmare and when I helped consult to a couple teams for a bit I was shocked at the slow speed everything gets done at. Many reasons for that, but it's hard to describe how unlike a typical fast moving startup it is (unless your not changing patient protocols).

2) ML is moving faster than it was 10 year ago. Way faster. So comparing work done 20 years ago is difficult, they just didn't have the same resources, ecosystem, and momentum.

3) It's only a hard problem in practice not in principle and well suited to ML. Unlike some problems where difficulty in practice doesn't match difficulty in principle (like warp drive space travel), the remaining engineering and open research questions don't suggest anything that will hit a wall, or prevent ultimate success on the order of the time frame suggested.

> It just takes longer in this field. Takes longer, but not necessarily more difficult (technically).

This immediately made me think "yeah they still use faxes", yet I don't think anyone doubts faxes are on the way out. Still "it just takes longer" understates the just how slow it is. It looks to me like a critical mass of older fax using doctors will have die off before the change can happen.

It does make you wonder how they get away with this. In most industries competition and cost pressures will force the change. Not so in medicine, apparently.

Over for radiologists?

Call me back when your machine can produce a comprehensive analysis of an anatomic configuration in relation to every element in the patient's file.

The real problem with those ML systems is that people don't understand what a radiologist is. Let's perhaps solve that problem first?

1) I understand there's more to it than that which is why I called out imaging analysis at some point during my post.

2) I said over "for the most part". There are still travel agents today you know.

Please understand, the argument is not that they will not exist, but that there will be a greatly diminished job market unless imaging demand grows tremendously. And also that the nature of the job will be much different and probably see salaries stagnate.

3) Given your "call me when" scenario I would need ask specifics to properly respond. However at a superficial level I'm not sure I see any kind of millennium prize class problem at first glance.

The job of a radiologist is to construct a story that holds water around the diagnosis and communicate that to other specialists, so they can use that to understand the problem at hand.

Concretely, a radiologist may say "there is a lesion compatible with your presumption of diagnosis X, however another neighbouring lesion Y speaks against diagnosis X. In light of patient history, further examination of lesion Y by modality Z is advised". That's the minimum we expect, otherwise we wouldn't need radiologists because many specialists can analyze the images relevant to their specialty themselves.

I don't deny it will be possible to automate in the future, but currently not possible. Radiologists are useful, and their job lies beyond matters of image description.

Sounds like we're in perfect agreement on the critical points and the only debate would be around the timeframe (10 years or ?).

- It will be possible to automate a large % of a radiologists job in the future, it's currently not possible.

- Radiologists are useful and their job lies beyond matters of image description

> People say, but psychology, fear, unknowns, will require human supervision indefinitely. Of course that's true.

The humans that supervise that will become the new radiologists. The "best" of those humans will have cross-field disciplines in ML model development and traditional radiology. My capitalist side sees a huge opportunity here in consulting and helping the existing radiology departments(who are interested) bridge the gap from current practice to a hybrid approach.

Adapt or die, etc.

- What about the point that the total headcount demand for these supervisors/proofreaders will likely be drastically reduced?

- The idea of cross-training radiologists to understand ML model development might be an ideal, but it's hard to see more than single digit percentages of folks who could make that transition. The jobs are way different and require years of ramp up. Even the mindset and the cultural approaches to work seems different, based on only a couple couple times I've worked on cross discipline teams.

But hey, it's still unfolding, I could be wrong about the consulting. If you figure out a model and how to sell it ping me, I'd be glad to provide feedback to help you refine it quickly.