A couple of contrasting use cases. Google Vertex and Recent Imaging study contrast maturity of AI models.
Today in health, it we're going to take a look at a couple more stories on AI. We're going to try to continue. To track the progress of this and what the major players are doing and what we might expect in the coming years. My name is bill Russell and the former CIO for a 16 hospital system and create, or this week health. Instead of channels and events dedicated to transform health care, one connection at a time. We want to thank our show sponsors who are investing in developing the next generation of health leaders. Short test are decide parlance, certified health, notable and service. Now check them out if this week health. Dot com slash today. Share this podcast with a friend or colleague, you said his foundation for daily or weekly discussions on topics that are relevant to you in the industry. They can subscribe wherever you listen to podcasts. All right, we're going to take a look at two stories. First one, Google, Google announces new generative AI search capabilities for doctors. By the way, any of these stories I'm talking about hit our website this week. Dot com slash news. We are using AI to generate this new site and we're going to be coming out with a daily. , newsletter, which takes news. Not that we write, but news from around the industry. Consolidates that and delivers it right to your inbox every day. So stay tuned for that. We're currently in beta with a few users. And working out the kinks on that, and soon we will be giving that to you. So anyway, , this story is front and center on their Google announces new generative AI search capabilities for doctors. Here's key points because it's a CNBC article. This is what they do. Google cloud on Monday, announced new artificial intelligence powered search capabilities that will help clinicians. Quickly access information from different data sources. It can be challenging for doctors and nurses to find information since it's often stored across multiple systems and formats. Amen to that. So Google's new tool. Brings it all to one place. The new feature will be offered to health and life sciences organizations through Google's vertex, AI search platform. The company said it will help save health workers, valuable time and energy. It's interesting. There was a Google appliance back in the day, and you could put this on your network and it would go out and scour your network, your internal corporate network, just like it does for the web. And it would find all this information and it would make it available. And it's, it was the same as any other Google search. You did the search and it would come back with different files and whatnot. I remember installing it at one organization. We pointed at a bunch of repositories. And you know, what you find out, you find out. Information that is available on your network. It was like a privacy security tool. The information that is available on your network that you didn't know was available and open to anyone, things like salary information and, and, , confidential emails and things that have been. Stored and saved. , so it was an interesting tool that way. This is a little bit like that, and that you utilize this tool. It goes out. It looks at your various, , systems collects that information. And then you can make a general query of the information and it will return. The information, regardless of what system it is stored, it. I thought the, , most interesting, you know, there's a little back and forth and they've talked about, you know, the growth of AI talks about. , some studies and that kind of stuff. , later on in the article, it has a great quote. So Mayo clinic is not using the new V vertex AI search tools in clinical care just yet. Said Chris Ross may oats. Chief information, officer, it is starting with administrative use cases. Again, , well, actually he goes on, we're curious. We're enthusiastic. We're also careful. He told CNBC in an interview. And we're not going to put anything into patient care until it's really ready to be inpatient care. , down the line. Ross said Mayo clinic is looking to explore how vertex AI search tools could be used to help nurses summarize long surgical notes sorted through patient comp patients, complex medical histories, and easily answer questions such as. What is the smoking status of this patient? But for now, the organization is starting to slow and examining where AI solutions like Google. We'll be most useful. It's interesting. This is what I'm finding. I'm finding at least in this generative stuff and on the consolidation of medical records, anything touching the clinical aspect, I'm finding organizations are being very cautious and that is perfect. That's exactly what we should be doing. In the clinical setting, we have to maintain our focus on quality and outcomes, and we should be moving slowly, , in, in administrative areas where you could have some errors and it not be catastrophic or even impactful. There are a lot of use cases where I'm seeing organizations push forward. I will say that AI is in, in different stages of development, different stages of maturity. Depending on what you're looking at. Right. And so in some areas we can really push the needle and the reason we can do that, Is because it's been around a little bit longer. Let me give you a use case would be imaging. Right. So in the area of imaging, we've been, , really pushing the envelope, pushing the needle. Yeah, it's early in the morning. Pushing the envelope. In, , in, in the imaging space, we've seen it in radiology, , somewhat in cardiology and whatnot. And, and, , and now we're seeing it at computer vision in rooms. I did an interview yesterday. With a deal Patel. , which will air, I don't know, a couple of weeks out on our conference channel. And he talked about the fact that they have gone from pilot to, , jet to full-blown, , deployment. Of, , cameras in the room. , attached just behind the, the, , TV in the room and they're using it for things like, , virtual nursing, , , remote visits and that kind of stuff. I mean, think about it. There's a lot of movement that has to happen in order for people to get to rooms and whatnot. So when you can have education and training and those kinds of things done. , remotely that makes things much more efficient. It takes me to my next article, which is breast cancer. Breakthrough. AI protects a third of cases prior to diagnosis. In mammography study. So AI could have the capability to pinpoint cancer diagnosis a lot sooner, a new study published by the journal of radiology last week, noted that AI helped predict one third of breast cancer cases up to two years prior to diagnosis. That is huge. Early cancer detection, , especially in the area of breast cancer is, , known to be just, , a significant. , predictor. If you get early diagnosis as significant predictor of good outcomes. So, , the research survey, imaging data and screening information from breast screen, Norway exams performed from January 20th, 2004 to 2019. Women who were later diagnosed with breast cancer, based on these exams were given an AI risk score by a commercially available AI system. According to the study's findings, the scores were ranked one to seven with low risk of Malaysia. , lignin C eight to nine for intermediate risk and 10 for high risk. And I score a mammography features such as calcification. And breast density were both assessed and tested in a total of 2,787 screening exams from 1600 to women. An average, , age of 59. The results revealed that more than 38% of the screenings detected. In interval cancer scored a 10 for AI risk proceeding. The breast cancer diagnosis. In cases of screening, detected cancers with AI scores available four years before diagnosis, 23% had a score of 10 for high risk. Steady coauthor. Oh, wow. , Norwegian name. , Solveg have finned head of the Norwegian breast cancer screening program and professor of radiology at Oslo. And. Akershus university college of applied sciences in Norway. I shared her thoughts on the outcome. We were surprised about the results, which means that a substantial portion of the cancers can be detected even earlier, as of today, resulting in less aggressive treatments and thus fewer side effects and late effects of treatment. Leading to better quality of life. She wrote in an email. , You know, it's it's , this is the promise of AI. And again, it's important to note different levels of maturity for different AI systems. Some of them have been around for years. Machine learning has been around for years, computer vision. , and the use of computer vision. In this case study of radiology and specific community. , specifically mammography. Is mature. These things can be pushed into your clinical setting. Things like generative, AI will need some time to mature. However, it can be used in areas where there's less risk. And that's what we're seeing. , the quality health systems do. They are taking the aggressive approach where it's appropriate.
They're taking a more cautious approach where it is not appropriate. So that is. Really what we're taking a look at this morning, two interesting stories. , Google's move and how Mayo is interacting with it. And then. The study out of Norway, I think shows the possibility of where AI can take us. And that's why we're excited about it. It's why we keep talking about it. All right. That's all for today. Don't forget to share this podcast with a friend or colleague. Do that today. We want to thank our channel sponsors who are invested in our mission to develop the next generation of health leaders. Short test artist, site parlance, certified health, notable and 📍 service. Now check them out at this week. health.com/today. Thanks for listening. That's all for now.