Welcome to another episode of Signal and Symptoms Podcast. November has been crazy for all of us. It's been insane. We had some guests come in and then we realized that we really need to catch up on some of these important things. So we're going to go ahead and give you sort of a feedback loop on what we have discussed till now and then move forward. Especially in the new year, we're going to plan more and more intelligence coming from right at the level of deployment and development. So Harvey, starting with you, you're an ABAIM course graduate, so am I. I think I did the basic. Did you do the advanced one yet?

Yeah, I did all and got all three certificates and then I'm faculty now with them or guest faculty.

I remember all you. Do you actually sleep,

My digital twin goes there and we can go there in a little bit and he takes care of things for me, so...

Again, so just to let you know, this was again, very thanks to Anthony Chang. I mean, the first book was Deep Medicine by Eric Topol. That was my inspiration. Then I found Anthony Chang and ABIM. This was the start of my entrepreneurship journey. Understanding how AI is so important is gonna change the world and we have been talking about this. But those who have not done it, I would highly recommend to take that course. Me, Harvey and Ed are working very hard to bring this course to Dallas. We don't know when where we will be working extremely hard to have something here in Dallas or our people here in Dallas, too so that it is available to multiple people. Okay, let's go to keynote stage, leading AI-driven transformation. I know you were not there, you were flying at that time, but when you are looking at HOLD's enormous potential, what was your key takeaways or what was the whole year? Think about it. I just want to bring these topics from this because those are excellently written. What would you recommend three key learnings this year and then next and how to sort of strategize for the next year with leading AI-driven transformation?

Yeah, well, I think one is to subscribe and listen to this podcast.

Love it! I love it!

That was not pre-planned and I don't mean it as a commercial, but it's true. There's not a lot of strong routine content out there yet. And so I love, Junaid, what you're doing. You're organizing us on a weekly basis and we're really pushing the envelope. mean, you're always presenting the latest and greatest coming out. I learned a lot. I'm part of this podcast team and I sit here and I just… learn a lot just listening and watching and all the discovery that you're doing. So the first thing is you've got to get in the game. And whether it's this podcast or some other sources, you've got to get in the game. The second thing I learned from being there and reflecting on the year is you've got to utilize the tools yourself. So it's great that you listen, that you subscribe to this podcast or other sort of learnings. But if you're not exploring yourself, you're not really learning, right? It's one thing we all know to listen and to observe, but it's next level if you actually do something. So everyone can grab something to learn. The example that you were giving on how you would create a strategy document, that's an example, but it could be more simple for some of the audience who haven't taken that step yet. It's as simple as, the next time you create a PowerPoint, leverage some sort of AI tool. And that's really the third thing is, you know, what's in your toolbox and it should always be changing, I think. I think that's the best way. So you might find a couple of applications, you know, there's my favorite, we'll have our favorites that we use, but don't get stuck on those. Always look for something new. What else is out there leading the way? So those are sort of my three takeaways. And I think if you do that, right, if you're always learning, you're gonna have your go-to sources to always learn, then you're applying these things and then you're mixing the two together, like you're not only learning, but you're applying and executing. I think that's the best thing. Those are the three takeaways from.

Harvey, three takeaways when you heard, and especially you were on the stage so much. By the way, I couldn't find much of your pictures, so you just send it to me and then we'll put it in the, I'm sorry, I should have taken pictures. I was just intent on listening on to you. So next time I'm gonna remember taking more pictures, but anyways, three very basic one-liners. If you are in an institution, imaging center, clinic, urgent care, emergency department, through this whole conference and the whole year, what are your three takeaways?

The first one reflect on Martec’s law, meaning our technology is exponential and our organizations, and I extracted down to our individuality can be very logarithmic, meaning the way we do things. And the take-home point is when those two become a huge gap, then a disruption happens. So apply that at your work, apply that to yourself. And so that leads to the extract point of you need to know these tools or else another company or organization will know them better and they will take over your company. And that is the truth that that will happen. Second point. It's look at these tools like riding a bike. The more you understand them, the more you're using them, the more efficient you'll be and the more comfortable you'll be. And then the last, the right limiting step is our culture ourselves. If you don't know what you don't know, you don't know how to use these tools. And so my analogy is look back. Look back when you first were looked at ChatGBT and your initial thoughts and how you use ChatGBT and now reflect on how you're using those today. And then ask yourself what changed? And the answer is the culture, the understanding, the education of that tool. And now you're accepting and doing things that you wouldn't do back. So those are my three points.

Well said. So three points for me. First of all, it was a very good introduction. And then from a startup perspective, just solve for pain points and then build solutions that are scalable and modular. So we developed ClinioOps, which is our IZMD. And then we have RedOps, which is our RadioView AI. And then we created a whole data operation system, which connects with any PACS.com EHR or large data exchange platforms. And then we are creating new value-oriented systems, which could be continuous opportunity monitoring, AI clinical analysis, AI quality assurance, whatever that case may be. And the one that we building in the process of building is acute care neurology, in which everything, both imaging findings, QA, virtual care, or physical care, legal operations, whatever the verification needs to be, etc. And a specific node navigators that are classic for this. So this is, and then we are of course creating an AI chat summary, which is going to be very important as we go along. And this is not only again applicable to one pin point. This is a system that we built that can use for personal injury, while you're doing two-way extraction for motor vehicle accident. So my thought process was that first it was an internal sort of, thank you for doing it, Junaid, that you have to build systems that can perform in an ever-changing world. That's what Ed said and Harvey said. Secondly, how would the software ever change? And that is what we built here at Save Life AI. And what we are trying to do over here is, as they said, that constant learning. I you a lot believe that how much I have to read in terms of research papers, everything, blogs, newsletters, et cetera, that you have to be constantly learning. I feel like there was a little bit less so Harvey in terms of nursing. Would you say that nursing is a little behind in terms of even as simple as AI scribe? And how were we going to, because they are, to be honest, would be more important than, I'm just being honest, and I know some physicians are going to hate me for it, but I cannot run an ICU without my nurses. And they're superb, amazing human beings. But again, Harvey, what would your thought would be? How do we bring in nursing, occupational therapists, physical therapists? By the way, my AI Stripe works for even a lactation nurse. But just letting you know, like how do you bring all of these different amazing providers, again, representing 20 % of GDP of USA into the fold of AI?

You know, really, Dr. Chang's good friend of mine and I love that he has this course and I think number one is the education, right? I always repeat myself, we don't know what we don't know. So by going by these courses and we understand now we start seeing, I really think it's elevating the AI IQ. The more we understand this AI, the better and the more likely we're gonna start using these tools. Yeah, I think there's definitely a few leaders out there that we know in nursing and that are sort of pioneering and leading the way. And I think we need a lot more. What happens, and it's not dissimilar to what we're talking about with PCP and physicians, is we get used to a certain way of doing things and the infrastructure behind us is a way of doing things. And so imagine if you're a nurse and you have all these great ideas and you get this, you could lead these courses, you go back to your traditional hospital, what are you gonna do? Hey, we're changing this completely. Then you're get unions involved. so unions are, as we know, I think we've probably covered some articles in the past, unions kind of get involved. Wait, time out, you can't do this. Danger, danger, danger. Why are they like that? Well, in part, because they're protecting jobs. So you have this whole culture in two different ways. One will be from unions that are… know, hyper-focused on protecting jobs, which I understand, and I'm not saying anything negative about unions, they got a good place in some areas, very, very helpful years ago and, you all that kind of stuff. But they're very hyper-protective of jobs and what AI is gonna do. And then again, your own culture. So if you're a leader and you're like, hey, AI is gonna change nursing and we need to adopt this and adopt that, you're gonna get a lot of pushback in your organization because wait, we're not structured like that. is whole transformative change. And that's why, again, I go back to my primary thesis that if we are unwilling to bend and change and be sort of aggressive on this timeline, something new is going to come in and disarm to mediate healthcare as we know it today. That's my prediction.

Yes, so true. And I have to reiterate it. The timelines are getting so shorter. I mean, I just showed you. mean, Gemini 2.5 and Gemini 3. This insane? Which brings us to the next question. Harvey, what do you think about DeepSeek and the China impact?

Yeah, good question. So for those people that are kind of touching this subject and understanding why DeepSeek was such a big deal, and there's some political stuff that's out there, but the skinniest is this. AI, look at it this way, spent billions of dollars as a company in general to do what they need to do. And I say billions, they said millions to create their models, but DeepSeq was able to do it at a fraction of what the chat GPT did, OpenAI. And what he's asking is, how do we see this? Well, the fact that they did it disrupted the market. In fact, the video chips went down, a lot of valuations went down because of this. Why does it matter? It's because people like us, now we can use this model like Junaid's doing, and he can look at it and say, okay, what's the secret sauce? How do they do it? And now the cost is not billions or millions. It's literally at times you can make this for hundreds of dollars. And so why does that matter? Because moving forward, if I could create the weights and I can put it on a phone and I could put it on smart devices and edge technology, or I could put it on a satellite or on a chip, now I'm able to bring down the cost. Now I'm able to do things at a really minimal cost

I was listening to another podcast yesterday while I was driving between some place and it was really on it had to do with China not not deep space specifically but it had to do with sort of on the political macro level how we're all leveraging these different LLMs and you know it used to be to the point that you were talking about it used to be all about open AI but obviously you know there's multiple languages in the world and it's not just English and so So it's very limited what you can get. And so these new language models are being developed at a macro scale that are in other languages and may surpass, you know, the capabilities that we're all familiar with models that we're all familiar with today. And because, know, that, yeah, the price have come down, you can do it much, much cheaper, take advantage.

Again, I want to reemphasize that you guys need to attend some of these meetings with us because, and hopefully support us, find the sponsors. We're to bring this to, you know, what do you call that, Dallas very soon. The question is the financial lifeline for healthcare. And this is again, who's going to pay for it. And I keep saying that, that this is extremely important to understand and how CMS, and I don't know if you looked at it, the new CMS guidelines, they just released the 2027. there's some striking problems that healthcare is going to fix. And as a matter of fact, the way that reimbursement is going to work, I'm actually going to be okay. I mean, being self-serving, my God, this is perfect timing for same like AI because either they're going to use AI or they're going to go back. But anyways, go ahead Ed who should pay for AI in general? Like this is a, and we're going to talk about US only for you and Harvey is going to take the international perspective and Ed, throw in your AI concierge perspective too.

Well, I'm glad, I'm not a big government person, but I was glad, you know, the article that you showed, the government's involved. And I think sometimes the government has to put out some motivation, help lead some research, you know, to gather some of the brightest and best minds that we have and really push things forward. So I'm a believer in that and I'm glad to see that research taking place. And so I think that's always good. I don't think we should rely on that. I believe in a great public-private partnership.

So how do we pay for this or, who pays for it? Honestly, it's interesting being outside the United States. When I'm in Singapore, I'm seeing tons of money being invested and used and they're really just jumping in and they're paying and subsidizing. And then obviously startups are just jumping on it and doing the same. And why are they doing it? Singapore is an interesting beast in the sense that their population is getting old and older more than the average in the United States. And because of the longevity, it's creating a stress on the product, on the population, because there's not enough doctors. And as a result, they're having to use AI. They're having to look at efficiency tools. But the skinny is this, they're spending money, they're investing in their company, in their government, in their people. One of the other thing is talking about the great shift. Anyone over age 40, they're retraining. Yes, they're retraining you so that you can leverage AI.