When AI Stops Being a Buzzword: Inside a Czech Hospital Where the Future of Healthcare Is Already Running
There is a moment in every technological shift when the conversation changes.
Not from if it will happen. Not even when it will happen. But to a far more uncomfortable and far more powerful question:
What do we do now that it’s already here?
That moment has arrived for healthcare. And nowhere was this clearer than in my recent conversation with Antonín Hlavinka, one of the key figures behind the most advanced use of artificial intelligence in Czech hospitals today.
Antonín doesn’t speak about AI as a futuristic vision. He speaks about it as infrastructure.
Quietly embedded.
Clinically measured.
Operationally unavoidable.
As Deputy Director for Innovation and Digitalisation at University Hospital Olomouc, the fourth- largest hospital in the Czech Republic, he sits at the intersection where medicine, technology, regulation, and human behavior collide. And that intersection is precisely where the future of healthcare is being decided.
From IT to Innovation: Why Hospitals Had to Change Their Structure
Antonín’s role didn’t exist ten years ago. In fact, it didn’t even exist in his own hospital until recently.
Originally responsible for IT, he witnessed something that many healthcare leaders underestimated: AI doesn’t fit into old organizational boxes. It is not “just technology.” It reshapes workflows, clinical responsibility, education, procurement, and even hospital governance.
That realization led to a strategic decision: to split IT operations from innovation and digital transformation.
This wasn’t cosmetic restructuring. It was an acknowledgment that AI demands leadership attention, not just technical maintenance.
Today, Antonín oversees innovation not only in the hospital but also teaches future doctors at Palacký University Olomouc, where AI, digital health, cybersecurity, and interoperability are now mandatory subjects.
That alone tells you something important:
The next generation of doctors will not ask whether AI belongs in medicine.
They will ask why it took so long.
The Real Adoption Curve: Students, Seniors, and the COVID Effect
If you want to understand AI adoption in healthcare, forget the hype curves. Look at behavior.
Among medical students, the response is split. Some engage immediately. Others resist not out of fear but because they are overwhelmed. AI is not yet their primary concern when anatomy exams are looming.
Among senior physicians, the shift has been slower but decisive. And the turning point was COVID. Before the pandemic, digital tools were treated as optional. After COVID, they became essential.
University Hospital Olomouc is also the National Telemedicine Center for the Czech Ministry of Health. What Antonín saw was striking: physicians and patients went from resistance to demand almost overnight.
Now, more than half of doctors actively use AI tools, including large language models for decision support, diagnostics, and documentation.
Radiology led the way. Not because radiologists are more tech-savvy, but because certified AI systems proved one thing doctors respect deeply:
They save time without compromising trust.
AI in Hospitals Is Not One Thing. It’s Three.
One of the most dangerous misunderstandings about AI in healthcare is treating it as a single category.
Antonín breaks it down clearly:
1. Administrative AI
This is where adoption is fastest and resistance is lowest.
At Olomouc, tools like Microsoft Copilot and secured OpenAI models support clinicians and administrators with documentation, summarization, legislation review, and internal communication.
Crucially, all data stays on European servers and complies with GDPR requirements, non-negotiable in public healthcare.
2. Certified Clinical AI
These are AI systems embedded directly into medical devices or imaging workflows, especially in radiology, CT, MRI, and mammography.
Some come bundled with equipment. Others, like solutions from Carebot, are software-as-a-service platforms already certified under EU medical device regulations.
Certification is slow, expensive, and paradoxical: by the time an AI model is certified, it is often technically outdated.
Yet certification remains essential for trust.
3. Experimental & Research AI
Here is where innovation accelerates.
University Hospital Olomouc develops its own narrow AI models, runs clinical pilots, and participates in EU-funded research, ranging from post-COVID syndrome prediction to microbiology systems that reduce bacterial identification from one week to one day.
These models are not deployed unthinkingly. Every project begins with proof-of-concept, KPIs, cybersecurity review, and ethical oversight.
“Is AI Better Than Doctors?” The Wrong Question.
One of the most powerful moments in our conversation came from a real clinical example.
A patient spent two months without a precise diagnosis. Specialists and biochemists exhausted traditional methods.
The case was then analyzed using a secure GPT-based model.
In 22 seconds, the correct diagnosis appeared, ranked first.
No drama. No replacement of doctors. Just clarity.
Another case involved AI detecting barely visible fractures on imaging, catching what human eyes missed and preventing severe complications.
These are not productivity hacks. They are outcome-altering moments. The real question is not whether AI is better than doctors.
The real question is:
Why would we deny doctors access to tools that clearly extend their capabilities?
Governance Matters: Why Innovation Needs Structure
AI adoption fails when enthusiasm outpaces governance.
That’s why University Hospital Olomouc created a dedicated Innovation Committee, a multidisciplinary body including clinicians, lawyers, data protection officers, IT specialists, biomedical engineers, and innovation leaders.
Any AI initiative must pass through this committee.
Not to slow things down but to ensure sustainability.
Projects are evaluated on:
Clinical value
Cybersecurity
Data governance
Cost-effectiveness
Regulatory feasibility
Only then do they proceed to pilot or procurement. This structure protects both innovation and trust internally and publicly.
The Human Side: Fear, Resistance, and the Workforce Question
Let’s be honest. AI makes people nervous. Administrative staff fear redundancy. Doctors fear devaluation. Public institutions fear political backlash.
Antonín doesn’t dismiss these fears. He contextualizes them.
Every major technology, from electricity to GPS, triggered similar anxieties. Yet productivity increased, not humanity decreased.
AI will not eliminate healthcare workers overnight. But it will expose inefficiencies. And that requires leadership courage.
The biggest opportunity?
Patient journey management, administrative automation, and decision support that frees clinicians to focus on what only humans can do.
Education, Children, and the Myth of “Lazy Thinking”
Antonín uses AI at home, too. Recently, his daughter used GPT for hours to understand geometry, asking it to explain concepts “like I’m a child.” The result wasn’t dependency. It was comprehension.
Yes, studies warn about cognitive offloading. But history tells us something else: tools don’t reduce intelligence; uncritical use does. AI doesn’t replace thinking. It accelerates feedback. And in education, that may be its most profound impact.
Europe vs. the World: The Ecosystem Problem
Is the Czech Republic leading Europe in AI healthcare? Not yet.
Antonín is candid: countries like Denmark haven’t adopted radically better tools, but they’ve built better environments. Supportive legislation. Clear pathways. Innovation-friendly procurement.
Europe’s regulatory rigor protects citizens but it also slows momentum.
The good news? The realization has arrived.
New digital health hubs, EU funding, and cross-sector partnerships are emerging. Olomouc itself is part of a national Digital Innovation Hub connecting hospitals, universities, government, and startups. It’s not fast. But it’s finally moving.
Five to Ten Years From Now: The AI Hospital
What does Antonín see ahead? Not science fiction but inevitability.
AI departments. Agent-based systems supervised by clinicians. Highly automated diagnostics. Telemedicine integrated with predictive analytics.
And patients?
Generational change will decide acceptance. Gen Z and Gen Alpha already interact comfortably with screens, avatars, and automation. For them, AI-first healthcare will feel natural.
For others, hybrid models will persist. The Rubicon, as Antonín put it, has been crossed.
The Leadership Lesson
This conversation wasn’t about technology. It was about leadership. AI in healthcare doesn’t succeed because models are more innovative. It succeeds because leaders are braver.
Brave enough to restructure organizations.
Brave enough to measure outcomes honestly.
Brave enough to confront fear without denying progress.
And perhaps most importantly:
Brave enough to accept that the future of healthcare isn’t coming.
It’s already running in production. And the only remaining question is whether your institution is leading it or trying to catch up.
Pavlina Walter