Okay. Well, I I can tell you where it's going because the technology is moving so fast. It's almost hard to keep up with the potential use cases. But to get it to be applied in the clinical practice and within a workflow requires, another type of emphasis, and that is thinking about what we'll call implementation science. Clinicians and patients, we care less about the technology and more about the outcomes that it achieves. So does it help improve the overall experience? Does it reduce cost? Does it enable me to become more access have health care more accessible? Is it improving care? Is it reducing, errors? I mean, these are the things that people care about. And the technology gives us the capability to do that, but it also has to be implemented properly. And so this whole area, what I'll refer to as implementation science, will probably become one of the next areas of focus for a lot of folk your organizations, understanding how can you actually apply this AI to generate the value that we know it can achieve. The other thing is you have to have people that can trust this. If if they don't trust the AI, it it may not, get adopted, and then that would be such a shame. And and how do you instill trust? Well, you have to have a level of testing and monitoring. There has to be some level of transparency. And that is the type of work that is currently ongoing to try to put these guardrails and these safeguards in to ensure that AI is doing the things that we know it should be doing and it's not going off the rails. That to me is is extremely important. And then there's this other element, is around education and training and and skilling and reskilling. And we've got an opportunity to completely transform health care. But there's two paths that we can follow, and I can tell you right now, it's not clear what will be the path that most organizations or individuals take. One is you can use AI to just completely automate things and and essentially replace the functions that people are currently doing, which is one path. The other path is we can think about how we can augment our capabilities and potentially do things that we could never have envisioned before. And that is the preferred path, but but it is so easy to look at AI and say, oh, I'm just gonna replace an individual. So I'll give you an example. So everyone talks a lot about how AI is as good as a doctor in terms of making a diagnosis. So, like, it you there's studies on radiologists, and they said, you know, this AI actually does better than a lot of radiologists in terms of identifying certain diseases. And the first assumption would be, well, we're gonna replace the radiologist with the AI. But let's think about that. There is actually a shortage of clinicians. There's a shortage of radiologists. There's a shortage of specialists. And when we start replacing some of the most valuable assets we have within the health system just because we can, we have completely missed the boat. I can give you an example. When when in, at Microsoft, I have a colleague who shared this story, publicly, and so I'll share this with this with you. And he said that he had an uncle who came into the ER with severe abdominal pain, and he died in the ER. His he had an aneurysm and it burst. His CAT scan would show that aneurysm was in the queue to be read by a radiologist. Let's imagine if AI, as soon as he got that that CT, it read it and immediately prioritized it. And so work AI working in collaboration with the radiologist could have led to better care. And that's not unique to the ER, but that could be done across any setting. And that's the opportunity that we have. We have to think about how we augment care and not fall into the trap of saying we're just gonna replace individuals. And to me, that's one of the next journeys, educating people about what's the art of the possible and showing them how to do it.