Charlotte Blease

Here's one of them, discrimination or bias in healthcare.

How good are we at actually de-biasing doctors?

I mean, we do sort of anti-bias training and people should monitor their own stereotyping and the American Medical Association advises that.

It's pretty ineffectual.

So we have to say if we really care about patient outcomes,

and things like face-to-face discrimination interfering with diagnostic accuracy or the treatment of patients.

Is there a better workaround to this?

And that's where I say, actually, it might be easier to de-bias some of these tools than it is to de-bias people.

Or you're putting a hell of a burden on a medical doctor in a, say, Dr. Phil parlance being the best version of themselves.

That's very challenging in the frenetic world.

We need a realistic, practical conversation about that.

It's such an interesting talking point.

So what I think has often happened in medicine is there has been this sort of leitmotif

There's no threat here because man and machine will work together.

And this is sort of Hallmark-esque, Hallmark movie kind of imagery of the doctor and the AI working well together.

Well, for years, there has been AI in the form of clinical decision support, and it hasn't worked out amazingly well.

Doctors tend to override AI.

because of the so-called last mile problem.

How do you actually get these tools to integrate?

That is the sort of $64,000 question.