Key Takeaways:
- AI speeds up HealthTech development but increases responsibility for safe design.
- Trust-first UX is critical for clinician and patient adoption.
- AI tools help with productivity, but strategy and compliance still require experts.
- Modular AI architecture allows faster iteration without regulatory risk.
- Startups often build models before validating real clinical workflows.
- Responsible AI integration is becoming the key HealthTech differentiator.
AI is reshaping HealthTech fast. How has it changed the way Halo Lab designs and builds digital health products in practice?
Sergiy: I’d highlight two main changes. The first is speed. AI allows us to build much faster, which is a clear advantage, but it also increases the complexity of responsibility.
Because of that, we no longer think only in terms of screens, we design systems. This means defining clear human-in-the-loop interactions, confidence indicators, AI reasoning summaries, and safe fallback states. It’s not just about whether a model can predict something accurately, but about how a clinician or patient can safely act on that output. This shift affects both UX and architecture.
Trust-first UX has always been fundamental in health tech. In practice, this means reducing cognitive load for clinicians and patients. For example, we’re currently working on patient dashboards for a U.S. hospital because the existing ones create too much cognitive load. For example, in our work with VitVio, an AI-driven operating room platform, we focused on designing interfaces that help clinicians understand AI-generated insights in real time without interrupting surgical workflows. The goal was not only accuracy but clarity and cognitive simplicity during high-pressure moments. Similarly, in the Rytmo cardiology platform, we redesigned monitoring dashboards to surface critical signals faster while reducing visual noise, helping clinicians interpret patient data with less cognitive effort. We also focus on clearly communicating uncertainty and limitations, rather than hiding them, and on avoiding over-automation. All of this directly impacts adoption and investor confidence.
Second, while AI enables fast experimentation, iteration in health tech must remain traceable. That’s why we build modular AI layers, clear logging systems, and version-controlled model integrations. This allows teams to move quickly without creating regulatory debt.
R2GConnect**: Interesting. Many startups believe AI tools can replace design or development work. **Where does AI genuinely help and where do HealthTech teams still need experienced partners, especially around UX and compliance?
Sergiy: AI tools definitely help startups for rapid UI drafts, code scaffolding, generating test cases, boosting internal productivity, and summarizing research or medical literature. They can significantly shorten early build cycles. Where experienced partners are still crucial is product strategy. AI can build features, but it cannot define intended use, risk classification, clinical positioning, or market differentiation, all decisions that shape everything that follows.
UX for clinical adoption is another key area. AI-generated UIs may look polished, but they often ignore clinical workflows, liability concerns, or uncertainty, and can even increase cognitive burden. In health tech, poor UX isn’t just inconvenient, it can be unsafe. In projects like Nyra Health, which focuses on digital rehabilitation, we redesigned the therapy journey so that AI-assisted care feels transparent and supportive rather than automated, helping both patients and clinicians maintain trust in the system.
Finally, compliance architecture is essential. AI typically doesn’t handle audit trails, risk documentation, data traceability, or post-market surveillance logic. Without careful planning, this can become expensive and painful later. Our role is to help startups move quickly without creating unnecessary compliance risks.
For medical devices and regulated health products, how is AI changing product design, validation, and iteration without increasing regulatory risk?
Sergiy: It’s really a question of balance. AI brings both opportunity and risk, especially in health tech, but there are ways to reduce that risk.
One key step is defining clear intended use early. We often guide founders to clarify whether their product is clinical decision support, a diagnostic tool, or workflow optimization. A precise intended use helps position the product in the correct regulatory class. For example, one of our customers, Omnibuds, developed earbuds capable of monitoring vital signs. In cases like this, we often help founders position the product initially within a lower-risk regulatory category while building the architecture to support future expansion into higher-risk medical device classifications.
Another important practice is modular architecture. We separate AI logic, business logic, UX, and logging/monitoring. This allows model updates without revalidating the entire system which is critical for startups that need iteration speed post-MVP.
Lastly, we follow validation by design. That includes performance monitoring, dataset version control, and drift detection. These mechanisms help startups demonstrate to investors and regulators that they understand long-term risk management, which is crucial in health tech.
R2GConnect**: While these strategies help startups manage regulatory risk within a given market, regulations vary by region, which can influence both innovation and compliance strategies. **How do regulatory approaches to AI in healthcare differ between the U.S. and Europe, and what impact does that have on innovation and risk management?
Sergiy: The U.S. tends to be more risk-friendly compared to Europe and is potentially less compliance-driven. Yet, healthcare in both regions is still highly regulated which is actually a good thing. For example, Europe’s new AI Act provides clarity on many AI-related questions, which helps the field move forward.
The U.S. has the benefit of very clear FDA approval pathways. The system is well-established and effective, making it easier to navigate regulatory requirements. That said, both regions are evolving every six months there’s new guidance and growing awareness.
Personally, I believe a reasonable level of regulation is positive. I often make the analogy with cars: you want your car to drive fast and accurately, but you also want the brakes to work perfectly. In healthcare, proper regulation ensures safety while still allowing innovation, because AI can have broad societal and patient impacts.
From what you see, what UX or product mistakes do AI-first HealthTech startups make most often and how do you help them course-correct early?
Sergiy: It’s true that VCs often expect health tech startups to be AI-driven, which puts pressure on founders to adopt AI. But we work closely with founders to ensure they build solutions that truly solve the problem.
One common mistake is building the model before validating the workflow. Technical founders can fall in love with the algorithm, but adoption depends on workflow fit. That’s why we start with shadowing users, mapping real-world frictions, and identifying decision pain points. AI should remove friction, not create new cognitive burdens. A good example is Hospity, a hospital operations platform where we redesigned the workflow for medical staff coordinating patient logistics. By simplifying complex operational dashboards and decision flows, we reduced friction for clinicians and administrators while improving real-time situational awareness.
Another issue is designing for “wow” instead of trust. This shows up in complex dashboards, overconfident claims, or autonomous recommendations. In healthcare, credibility is everything. We help founders tone down risky claims and design for realistic usage scenarios, focusing on adoption and retention, not just technical performance.
A third area is ignoring failure states. AI often fails on edge cases, but many startups design only for ideal outcomes. In health tech, that’s unsafe. We ask questions like: What happens when confidence is low, data is incomplete, or predictions conflict? From day one, we design for graceful degradation.
Finally, there’s over-automation. We usually position AI as augmented intelligence, not fully autonomous decision-making. This approach reduces regulatory exposure and increases adoption. In fact, many problems can be solved with basic automation-sophisticated AI isn’t always necessary. Keeping things simple is a good principle, especially in regulated health tech environments.
Halo Lab works across UX, AI, and engineering. What problems do startups typically come to you with after trying to build on their own or rely heavily on AI tools?
Sergiy: One common issue is that the product may work technically, but users don’t trust it, which shows up in poor onboarding, adoption, or retention. Often this is due to poor explainability, overconfident UI language, or lack of visible validation. In these cases, we redesign the experience around credibility. We often address this by designing transparent AI feedback loops, confidence indicators, and contextual explanations so clinicians understand why the system suggests a specific action. For very early-stage startups, compliance is often overlooked. We help by retrofitting documentation, restructuring data flows, implementing audit systems, all to accelerate funding and certification.
Another frequent problem is that AI is disconnected from value. The model exists, but there’s no clear clinical ROI or differentiation. We reposition AI around a value proposition tied to outcomes. Since we’re a UX-driven product development company, we also support branding, positioning, pitch decks, and marketing material, a service our clients increasingly request and that’s growing rapidly. Many early-stage founders also come to us because their MVP doesn’t scale. AI prototypes often fail under real-world data variability, so we focus on production readiness and scalability, including monitoring and refining for robust performance.
Can you share some use cases?
Sergiy: Sure. One example is VitVio, an AI-driven operating room platform where we designed interfaces that allow surgical teams to interact with real-time AI insights without disrupting clinical workflows. Another example is Rytmo, a cardiology monitoring system where we redesigned dashboards to reduce cognitive load for clinicians interpreting patient data.
We also worked on Nyra Health, a digital rehabilitation platform where the focus was on making AI-assisted therapy feel transparent and supportive for both patients and clinicians. In hospital operations, our work with Hospity focused on simplifying complex coordination workflows for medical staff.
Finally, with Omnibuds, a medical wearable that monitors vital signs through earbuds, we supported product and UX design for a novel form factor that blends consumer usability with medical-grade monitoring.
For founders scaling in the next 6-12 months, what’s one practical decision around AI, UX, or compliance that will save them the most time and rework later?
Sergiy: The most important decision is to define AI’s role in clinical decision-making before finalizing the architecture. Specifically: is it assistive or determinative? Does a human always review the output? Is it advisory or diagnostic? This one decision affects regulatory pathways, UX design, risk documentation, liability exposure, and even investor perception. The most expensive mistakes we see in startups aren’t technical, they’re strategic ambiguity. From what we’ve seen, AI itself is no longer the differentiator in health tech. Responsible integration is. At Halo Lab, we help founders move fast without creating product, regulatory, or trust debt that slows them down later.
R2GConnect: Thank you for the insights, Sergiy.
If you're a HealthTech founder preparing for fundraising, refining your product experience, or looking to better communicate your product’s value to investors and users, Halo Lab offers several opportunities to support you from pitch deck improvements to branding and landing page design. Exclusive Complimentary Offers (Deadline: March 19th, 2026)
Service Packages (Deadline: March 31st, 2026)
- Design of Fundraising Assets (esp. Pitch Deck) Included: Up to 15 slides (additional slides can be added at an adjusted price); 2 rounds of iterations. Fixed price: 3,000 EUR / 3,500 USD. Learn more here
- Starter Branding Deliverables: Brand Identity and Logo Design. Fixed price: 6,000 EUR / 7,000 USD. Learn more here
- Landing page design and development Deliverables: Webflow-based website with 8 blocks page and basic animations. Fixed price: 12,000 EUR / 14,000 USD. Learn more here