you guys have laid out the promise of this approach. Let's talk a little bit about the potential peril, Carlos. We're talking about what to do when you find a variant that has an unknown clinical significance. And there was a lawsuit Oregon, where a woman had hysterectomy because her physician misinterpreted the genetic test. Yeah, that's right. And it's really unfortunate because this is a very common situation where she had a family history, she got a genetic test. The genetic tests actually came back negative, but they included in the Test information. That said that there was a variant That was found that was unknown, significant. And so the test very clearly indicated there was no clinically significant mutation found by the sort of practising guidelines. It was a negative test yet for I think reasons that these to me are are unknown. The recommendation for her was that she would have a bilateral mastectomy and the hysterectomy And it's I think it was a particularly sad because the mutation was actually in a gene That's not associated strongly, at least with breast cancer. It was in M L H one I believe. And you know there's a strong association with colorectal cancer and into mutual cancer, But there really was no strong support for a breast cancer Association. To me, this poses the challenges of how complex really this data has gotten to communicate even two physicians and then to have that message go clearly to patients. That's incredible story. So look thinking about freedom. Perhaps this is unfair comparison or an example. But if we think about early screening, we've been using mammograms for thirty forty years. And the data suggest that while we've actually done a lot of early detection of breast cancer using mammography, The number of late stage cancers a breast cancer is actually hasn't gone down. So that implies that we've actually over diagnosed things or in some cases diagnosed the wrong things. Is there an analogy here or is that a worry for you, and if so, how do you control it? So there's there's a lot of concerns in terms lunch and a diagnostic. I think what you're talking about is really under technology to sign side of things, right? And this is one of the really interesting things for us because a breast cancer and specifically mammography as a screening methods has a false positive rate of fifty percent, fifty percent fifty percent. So from a false positive perspective, you're better off flipping a coin than doing a mammography really. And there's a reason for this, which is that no clinical trial that we've done has ever been large enough. You don't actually compensate for all the false positive cases that could potentially happen all the false negative cases that could potentially. Happen in a clinical trial. And so once you launch dis diagnostics into the market, The only direction the performance goes is down. It really doesn't really go up Ryan because there are all these Edge cases that you didn't account form for the first time in our history were able to compensate for that problem where because we can now make a diagnostic that's fundamentally a I based even after we launched the test into market, we can actually work with our partners to get results of the test that we sell back so that we can teach the artificial intelligence that it made mistakes after a l- sorta launched a pass and it never makes that mistake again. So for the first time, we have an opportunity to make the direction of the accuracy of a task after we launch, it go up as opposed to go down. And that's really the only way we're going to compensate for this Because the largest clinical trial that's ever been announced but hasn't been perform yet, is hundred twenty thousand people. Last year thirty five million. People should have gotten screen for colorectal cancer in the United States alone and didn't 35-million 35-million. Right. So if you can capture even a fraction of that market and learn from that information and make sure that you don't make mistakes from those tests ever again. Then all of a sudden you have a clinical trial That's an order of magnitude, if not to greater than two largest clinical trial that's ever been announced. So we are both doing it. So potentially transformative for healthcare system. Who pays for this, How does this Get covered? Especially on the diagnostic side, reimbursement is a particularly tough issue. There's so many stakeholders in healthcare that it is not clear who pays for it. If you're looking at the average statistics, generally when you're launching a diagnostic test only about twenty percent of the test that you saw actually get fully reimbursed. So eighty percent of test that you're selling is actually not being paid for properly. Long story short is most of the time payers don't see a clear. Turn on investment that the diagnostic tests represent. So if I pay five hundred dollars for this test now, Am I actually want to make them money back? Because I detecting this disease earlier, So we don't have to spend as much money curing this person. And when the diseases progress further, that's a question that we need to be able to answer clearly before we can get these tests paid for. By the pairs, I think were like stuck in this model where we're relying on pairs to pay for these tests. There are new models that are coming out That's leveraging life insurance companies That's leveraging these closed systems where these hospitals, their own pairs, that are easier to sort of convinced of the value of the tests. I think we're going to see leveraging of these new models much more, but it's still under early days. Excellent. Thank you, Carlos. Thank you. For being here. You're working in a fascinating space and it clears. You're going to change how we think about disease forever and for always So thank you. Thank you. Thank you guys.