 I think given the fact that the deep learning algorithm has become more mature, there is a lot of effort driving this application towards Pacifica in the context of today's healthcare. And then from the technology perspective, we see already a lot of use cases based on computer vision. And now we see more and more development in the natural language processing and also speech tag recognition. So from that technology perspective, we definitely see more application in that field. From a diagnostic perspective, we see people starting from early diagnostics now moving into pathology screening and maybe clinical decision making support system. So it's going deeply into the clinical decision scenario more and more. I think people are starting by using public available data to train their algorithm. But when it comes to real application, you know, going to the hospital, we actually need more sort of underground data. Having said so, it's like the data availability becomes one of the bottlenecks to improve our algorithm. I think that's one of the challenge. The second challenge being that, you know, the current AI products are not fully integrated into the current clinical process. And you know, to an extreme that the physician might be able to complete the end-to-end process without having to use AI products. And that can create a gap, you know, in how we envision AI to be deployed in the hospital and how the real case is. So I think that's the two sort of major disconnect here. I think it's very critical because a standard set or a benchmarking rule allows software companies or a developer like us to really understand and then to objectively evaluate how good the algorithm is. And from there, we can continue to improve that with a quantitative measurement. And then also that allows people, you know, along the world to work together to a one goal. So I think that is critically important. My first impression is that the good weather engineer actually coming from Shenzhen is humid and hot. But I do see a lot of people from diverse background, physicians, you know, AI scientists, policy makers, professors. And I think people are all coming here to drive a very, very important initiative, and I can see a lot of efforts coming from different angle, different perspective, put it into this good workshop. And I definitely will see a great, I feel like we will be able to have a very fruitful discussion, you know, in this couple of days.