Digital Twins in Health AI Developement

This blog is a series of linkedin post on Digital Twins in Healthcare & Health AI Product Development, authored and reviewed by Paula Salme Sandrak and Ghaith Sankari. ✍️ As product developers focusing on health AI, we understand the potential as well as the obstacles in developing health AI products. We will be publishing on a number of exciting topics to explore these opportunities and challenges over the coming weeks. 📅✨

Let us start by basics:

What are Digital Twins in Healthcare? Unveiling the Basics

Ever wished you had a twin to tackle life's challenges? 👯‍♂️ Someone who’s on the same page with you but sees past the many oversights and biases we all have about specific life situations; someone who can zoom out from the narrow moment and tell you where to go because they have already been there? 🧭

Well, the health AI world has its own version of this superhero duo: Digital Twins (DTs). 🦸‍♂️🦸‍♀️ Imagine a digital doppelgänger of yourself, but instead of swapping places and confusing your friends, this twin is a high-tech replica of your body, packed with relevant data about your health.

And no, it’s not just your steps or sleeping hours or HRV, but much more precise physiological information that would allow – among other things – physicians to test treatments on your digital stunt double before trying them on the real you. 🧪👨‍⚕️ 

• Would the meds help? 💊 It’ll be a lot clearer before you need to pop that pill. 

• Side effects? 😣 You’ll know beforehand. 

• How might you look if you tweaked your lifestyle, for example, eat less X and more Y, and ditch smoking? 🥑🚬Wow, dayyum, look at youuu! 😘 Newborn motivation.

So, what exactly are Digital Twins? 🤔 Picture your health as a super complicated video game. 🎮 Your digital twin is like the ultimate character, updated in real-time with all your health data – from blood pressure to hormone levels. 📊🩸 In fact, Digital Twins can be created to replicate anything, not just humans. They’ve been used in various industries for a while - manufacturing, aerospace, urban planning… 🏭✈️🌆 It’s just that in healthcare, the technology is taking its baby steps. 

Why should we care anyway? Think about the healthcare challenges we face:

• 🔮 Predicting chronic and acute illnesses so we can address them before they happen, 

• 👨‍🔬 Personalizing treatments so, for starters, you wouldn’t get that one-pill-for-all type of drug that was first studied only on men, in the 1990s… , or

• Avoiding those endless trial-and-error medication routines - when you're already on a cocktail of meds 🍸💊💊💊, to name a few… 

Properly trained and highly validated Digital Twins could simulate our health journeys, predicting how we’d respond to different interventions. 🤖 

But, let's not get ahead of ourselves. Creating these digital counterparts involves wrangling huge amounts of data (real structured data, not “health information”) and ensuring privacy, not to mention the sheer complexity of replicating a living human. Yet, the prospect is undeniable. 💫 

The Intersection of AI and Digital Twins in Healthcare: What Are We Missing?

🚀 While Paula nailed the definition of digital twins in healthcare and their potential, a fair question may pop up: why do we think these digital twins would work as expected?

Hold on a second! 🐭 Haven’t we been using twin-like models in healthcare for a while now? We test new drugs on mice—our biological “twins”—and use electronic health records as digital mirrors of our health information. 💻

With this in mind, “digital twins” can naturally be applied to replace or complement these “older models” in areas like R&D, understanding the patient journey, diagnosis & prognosis. The key difference compared to previous generation solutions is to develop accurate digital twins capable of fully simulating complex systems like human organs or diseases, and doing so responsively based on environmental inputs and changes. 🌟 How do you do that?  🎉 Enter AI!

With all the buzz around Gen-AI, it’s clear that AI tools have the power to automatically analyze and extract insights from various forms of health data—perfect for developing the digital twins we need. Digital twins, in turn, offer a highly accurate and cost-effective environment for validation and testing predictive models. 

This reciprocal connection can show up favorably in multiple stages of health AI development—like UAT or various validation steps. It’s clear that the marriage of AI and digital twins in healthcare is the ultimate power couple! 💪 But, just like in any relationship, there’s still some work to do and a fair share of drama🤯. 

🛠️ Here’s the scoop:

-Data Dilemma: Digital twins need a feast of data, but can we serve it up right? 🍽️

-Personalization Perks: AI promises personalized care, but can digital twins fill the gap to make it truly understandable and fair? 🌈

-Ethical Quagmire: AI in healthcare raises big questions. Who owns the data? How to ensure privacy? 🕵️‍♂️ Tricky waters ahead!

-Real-Time Riddle: real-time updates! Sounds awesome, but can we really use these updates to boost twin behavior and create more accurate simulations? ⏳ 

Phew, potential & obstacles! Let’s put it simply: 

Just like a bodybuilder’s supplements can give an instant boost, AI and digital twins can supercharge our healthcare system—enhancing capacity, improving service quality, and elevating the patient experience. 💪 

But here’s the catch: Without a solid development lifecycle, strong governance, and collaboration, between biological and digital scientists to face the mentioned drama, we risk turning digital twins into nothing more than flashy posters!

Trade-Offs of Using Digital Twins ? 🤔

Something’s been puzzling my mind for a while. I figured it’s time to share it with you all. 

After diving into the benefits of digital twins in health AI, we need to get real for a moment. The enthusiasm and excitement is contagious, but folks like me— developers who love a good challenge 😅—might not always make the most practical business decisions… 

:

It’s absolutely not fun to always play it safe, but some things need to be weighed together when embarking on with new technologies. Digital twins bring a whole lot of business controversies, such as:

• The Cost vs. Benefit Question (Planning Phase) 💸: On the one hand, DTs offer a way to simplify and customize models that can be reused in numerous repeat scenarios. On the other, creating these twins can be expensive. Where's the balance between the upfront cost and the long-term benefit of these DTs?

• Speed vs. Trust (Development Phase) 🤝: DTs and other simulators can save time by making medical validation and testing more efficient, but the catch—are development teams ready to trust these new tools? What do we need to do to build that trust?

• Proactive Development vs. Operational Costs (Maintenance & Monitoring Phase): Unlike traditional methods that tend to be slow and reactive, DTs identify issues early and accelerate the development process. However, they require continuous monitoring, updates, and debugging. The key question is how feasible is it to establish the necessary technical infrastructure to support this without significantly increasing costs?

• Data Privacy vs Faster Development (The Extra Nugget) ⚖️📋: DTs can enable decentralized development of health AI by keeping sensitive health data stored locally at different sites, rather than in a central database. This reduces the risk of data breaches and offers greater control over patient data. However, it also slows down development due to the need of synchronization and secure communication between multiple locations.

Are we willing to accept longer development times in exchange for stronger data privacy in the long run?

Adopting DTs can’t be a spontaneous decision. It requires careful planning—determining which areas will truly benefit from DTs, which won’t, and which savings they’ll offer to justify their development/acquisition and operational costs. It’s clear that DTs in health AI are more of a strategic choice than an operational one. 🧐💡Not as easy as adding a cool new extension to your new wide-screen TV in the living room! 📺😂