We live in an era of medical innovation—where genome sequencing, 3D-printed tissues, and remote surgeries are transforming patient care. What once seemed like science fiction is now reality, with healthcare professionals achieving incredible breakthroughs every day. But behind these advancements lies a paradox: as medicine reaches new heights, doctors, nurses, and clinicians face growing challenges. Explore the cutting-edge of healthcare and the realities shaping its future.

Video Transcript:

Andreas Cleve - Co-Founder & CEO Corti
We live in an age of medical miracles. Today we can sequence a genome in ours, print human tissues, and perform surgery from across the globe. Treatments that seemed like science fiction just decades ago are now part of our reality.

Healthcare professionals achieve what previous generations would've called miracles every single day. Yet behind these incredible advances lies a painful paradox. As doctors, nurses, and clinicians break new ground, they face unprecedented burnout. As patients rely on them more, we risk losing them. By 2030, the world will face a shortage of 10 million healthcare professionals at every stage of care. From primary visits to emergency response practitioners grapple with mounting administrative burdens and decision fatigue and the data.

It paints a sobering picture for us. 68% of clinicians work unpaid overtime. Every month, 58% of healthcare professionals experience burnout with 41% experiencing it monthly. 28% are prevented from delivering the highest standard of care due to burnout. And every week, 25% of healthcare professionals consider leaving the field all together. 

AI is helping. In some cases it's reducing documentation time by up to 80%, but many AI projects are stuck. While some general purpose AI appears to be outperforming human doctors in the lab, in the clinic, it's a different story. Healthcare systems and their IT departments are often concluding that this same AI that beats doctors in the lab is too expensive. It's not built for their workflow, nor is it safe enough.

So why is it that we're failing to go from concept to clinic because there's a fundamental mismatch. Healthcare is a field of specialists and we're applying general purpose tools to its highly complex needs. While chat, GBT has accelerated global AI adoption, healthcare cannot rely on AI systems trained on everything from society columns to social media.

Think about it. Would you use a Swiss Army knife for surgery? Of course not. Healthcare requires specialized tools for specialized needs. General purpose AI does not work in healthcare. Already we're seeing early signs of new problems emerging. A third of the healthcare professionals using AI in the US today are spending up to three hours a week correcting it. 

Picture this. An ER physician in LA overwhelmed with four hours of daily documentation, adopts AI for relief. At first it helps, but then hallucinations creep in. Now she's fact checking AI outputs and juggling systems instead of focusing on patients. Her trust in AI and its utility begins to erode. Hallucinations can be frustrating in casual settings, but in healthcare they can be catastrophic. That's why healthcare deserves better. Healthcare needs specialized AI because we wouldn't trust our health to a generalist. Healthcare needs an AI that truly understands. Understands that a positive test doesn't always mean that the test is truly positive and the discharge is not something to Dutch. 

Today marks a pivotal opportunity in healthcare AI. We're excited to finally unveil K'S Foundation models to all of healthcare. This is an industry first AI infrastructure built from the grounds up for healthcare professionals to support healthcare workflows and healthcare only. Our AI tackles the intricate demands of healthcare that only a specialized models can prioritize.

Firstly, healthcare demands AI build for purpose. That's why we've spent four years training our models on hundreds of millions of hours of domain specific data. By focusing solely on healthcare, we're eliminating the noise leaving only insights that matter.

Secondly, healthcare language is its universe of its own. We've built deeply nuanced understanding of medical terminology tailored to over 50 specialties and 10 languages. So REI speaks the language of care fluently. 

Thirdly, in healthcare, mistakes aren't an option and patient data has to remain sacred. Our infrastructure is highly accurate, compliant, and built to meet the highest global and local standards for both patient and data safety.

Finally, healthcare means every moment matters. Our AI is a lightning fast built to reason, in real time augmenting healthcare professionals to power care without compromise.

Let me introduce you to our three models, the new foundation for healthcare AI. First meet solo, the model that lets you build expert note-taking ai, giving you back hours of documentation time each day. It handles medical terminology across 50 specialties in more than 10 languages with unprecedented accuracy.

Next, meet ensemble. Ensemble lets you build AI agents that turn words into actions. It transforms live discussions into structured medical documentation that is significantly more accurate than general purpose AI. 

Now for Symphony, the world's first streamable AI model for healthcare symphony is 35 times faster than general purpose models, which means it's fast enough to help healthcare professionals in real time.

While in the moment with a patient, these models will remove dozens of administrative burdens faced by millions of healthcare professionals worldwide. If a rural clinic in France needs an extra pair of hands to take notes, that's solo. If a doctor needs more time with the patient instead of paperwork, try ensemble for ambient documentation. If a nurse needs to check clinical guidelines before making a decision, step it up with Symphony.

Our foundation models are built on nine years of peer reviewed research, and we know we're not just building technology, we're trying to build trust back into technology. This isn't about replacing healthcare professionals, nor is it about rushing into quick fixed solutions. It's about creating an AI infrastructure for healthcare that elevates and augments practitioners into critical work with burnout rates, soaring and administrative burdens, consuming precious hours, rebuilding trusted technology that returns healthcare professionals, their most  valuable resource time with patients because those who have dedicated their lives to saving others to serve AI as dedicated to supporting them. 

Now here's my co-founder, our chief technology officer, Lars Maaloe.

Lars Maaloe - Co-Founder & CTO Corti
Thank you. Thank you, Andreas.

You know what keeps me up at night? It's not the technology we are building, it's the responsibility that comes with it. Nine years ago when Andreas and I founded Corti, the promise of healthcare AI shown bright. But today as CTO, I see a landscape that deeply concerns me.  We could be in the midst of a golden age. Instead, an AI gold rush is flooding the market with well-intended solutions that are failing our healthcare professionals. In ambient scribing alone, there are now over 60 apps to choose from.

The promise of AI is to solve some of healthcare's biggest problems, but many apps are inviting new complexities. Instead, every part of the healthcare professionals workflow is about to become AI enabled. If that means lots of new apps, it will simply not work. The difference in complexity between an out of the box fast 
to build AI and an AI that has been carefully built to solve an expert task is significant. Similar to the difference between a specialty doctor and an intro level med student, good enough will never be good enough.

In healthcare, the stakes are simply too high. 75% of healthcare professionals support AI use in practice, but over half say they wouldn't feel confident using the current solutions. Healthcare systems are stuck in what we call pilot paralysis. AI projects are failing to launch, unable to scale beyond trials due to challenges with accuracy, cost and integrations. The problem general purpose models have been quickly implemented to form the backbone of healthcare's AI infrastructure.

I believe any school teacher that has seen GBT four plagiarized essays will agree to the fact that such essays will be brilliant at reaching the uninspiring level of mediocre AI offered for specialized tasks. Today is ironically very unspecialized general purpose models are impressive and versatile. But like Jack of all trades who know a little bit about everything, they are the masses of nothing.

In healthcare, this creates tools that miss critical nuances and make unreliable notes tools that are dangerously opposed and fail to integrate into legacy systems never designed for the complexities of healthcare. General models have over promised and underdelivered providers are left spending months piloting solutions that don't quite fit. 
Instead of taking flight healthcare, AI is grounded, unable to move from concept to clinic.

This isn't progress. This is AI pilot paralysis. Our foundation models are trained on something we call AI in residency inspired by the concept of medical residency. Just as medical residents progress through structured levels of responsibility, our models are designed to grow in complexity and reliability, proving themselves capable before handling more high stakes tasks. They're deeply trained and honed exclusively for the complexities of healthcare learning from the context of real life patient interactions, peer reviewed papers and medical data.

Unlike AI built to beat human doctors on specific exam papers, they have not been developed to lead an index. Instead, they're trained for the tough realities of healthcare to actually help where healthcare needs it most. To firefight administrative burden and burnout. Risk plaguing healthcare cots foundation models are built and trained so that every output is tested and validated in the healthcare domain.  They're traceable, auditable, and compliant with relevant healthcare regulations.

The results speak for themselves. We are 25% more concise than GPT-4, supporting time poor clinicians with precision. 20% more accurate than GPT-4 in healthcare context as accuracy. Here is ethics 35 times faster than GT four because in healthcare seconds can mean lives earlier. Andreas unveiled the first healthcare AI infrastructure of its kind, comprising three new models exclusively available to healthcare systems Solo. A fast model with audio reasoning, creating expert agents handling complex medical terminology in over 10 languages while integrating evidently with existing systems.

An example, a powerful model focused on exceptional documentation, creating expert agents that turn consultations into action, transforming medical discussions into documentation that is 25% more concise and more accurate than general purpose. AI Symphony merges powerful reasoning with speed. It is a streaming model operating 35 times faster than GPT-4 and provides evidence-based insights during patient consultations showing true agent behavior.

Here is where it gets exciting picture, a medical center of excellence, but in AI form on top of our foundation models, we've built 20 expert models, specialized capabilities that augments each foundation model. Think of them as medical specialists, coding experts, quality control experts, summarization experts, each one purpose built for healthcare, compliant by design and ready to integrate into existing workflows.

I'm now joined by Dr. Lasse Krogsboll, who is a general surgeon and has worked clinically for 14 years. Luckily for us, he now works on the product team at Corti. 

Lasse, you've been out there in the real world. Give us some examples.

Dr. Lasse Krogsboll - Corti Product Specialist
Yeah. We've been implementing this in a wide variety of settings and use cases, and one example I'll give is from a leading private healthcare provider in several countries that have been using our models for summarization of the clinical visit. And they were able to drastically reduce admin times doing this. And we also achieved a 100% user satisfaction rating. And in my career, I've used a ton of, uh, 
healthcare software and to be honest, I really didn't like much of it. So I'm really happy that we're able to give users something they actually like. 

Another use, uh, case is outpatient clinics in Sweden that used our model for coding. And coding is a task that is, uh, tedious and it's time consuming, but on the same time, it's uh, really important for billing purposes. And that's a terrible combination if you, a busy clinician trying to treat a lot of patients. So in this case, we're able to increase the quality of the coding and cut in half the time the users spend on creating the codes. 

A third example is, uh, emergency call center services, where a customer in the United States use our model to help the call taker discriminate. Is this a true emergency that requires a proper emergency response or is this not really an emergency and can be safely handled by an online nursing advice service? And we're able to optimize this emergency workflow by freeing up resources, by providing, uh, more than 50% increase in the number of calls we could safely route to the nurses. And if we can do, uh, emergency calls, then we can do a lot of things because the stakes are very high in that setting.

Lars Maaloe - Co-Founder & CTO Corti
Makes a lot of sense. Thank you so much, Lasse. What's remarkable isn't just the performance, it's the flexibility. We're using the same infrastructure across all these use cases just configured differently for each need. Think about electricity. We don't expect hospitals to generate their own power. They plug into a reliable grid. Similarly, we don't expect every healthcare vendor to build their own AI infrastructure. They should be able to plug, pluck into a trusted healthcare specific AI grid.

At Corti, we've built that grid. Our infrastructure is built for healthcare from the ground up, compliant with regulations, ensuring safety and trust, flexible supporting multiple specialties, languages, and use cases scalable, working across hospitals, clinics, emergency services, and more cost effective, making cutting edge AI accessible to all easy, ease to trust, ease to integrate, ease to buy, and ease.

To understand, let's create a connected interoperable ecosystem that breaks out of pilot paralysis thanks to a specialized high quality infrastructure. One that's elegant in its simplicity, effective in its execution, and most importantly, one that works seamlessly in the everyday moments where it matters the most. The stakes are simply too high to continue with solutions that don't quite fit. 

It's time for AI to graduate from endless pilots to everyday practice to move from promise to performance. Together we can make AI what it should have been all along an indispensable, invisible ally flowing through healthcare systems, electric, elegant, accessible to every clinician in every clinic, everywhere.

Thank you.