Building “Life Value” in Healthtech Through AI: An Interview with Alex Szilagyi, CEO of Life Value

Life Value: Creating Technology That Improves Real Lives

Alex Szilagyi traces the origins of Life Value (lifevalue.com) back to 2018 in Oradea, Romania. Originally operating under "Tech Stack Apps" brand, the company was founded with a simple but ambitious mission: to help Western European companies launch digital products quickly and reliably.

“Over time, we evolved into a healthcare-focused software partner,” Szilagyi explains, “but our purpose remains the same: to create Life Value - technology that improves real lives, not just processes.”

Today, Life Value specializes in AI-driven healthcare solutions, ranging from electronic and personal health record apps to AI-powered diagnostics. The company serves a wide spectrum of clients, from early-stage startups to established medical companies, combining technical expertise with a commitment to meaningful impact in healthcare.

Integrating AI into Healthcare Development

Alex Szilagyi explains that Life Value began adopting AI as healthcare clients expressed interest in predictive and diagnostic tools. A notable example is DentByte, where the company developed a virtual AI assistant featuring hands-free voice commands, smart scheduling, and patient workspaces. These capabilities reduced administrative workload by up to 30%, while the AI model continuously learns, improving diagnostic accuracy by 25% and helping dentists personalize care plans.

Beyond client-facing applications, AI also enhances Life Value’s internal processes. Szilagyi notes that the team uses AI-assisted coding, testing, and documentation tools to speed up development timelines and maintain quality across projects. In both client solutions and internal workflows, AI has become a practical tool for improving efficiency and precision in healthcare technology.

Where AI Makes the Biggest Impact

According to Alex Szilagyi, AI has brought the most noticeable improvements to quality assurance, code optimization, and data analysis. AI-powered testing tools now catch edge cases faster, while intelligent code generation and refactoring streamline development workflows.

He adds that AI-driven data analysis and model training have also improved precision and efficiency across healthcare projects. “This means we can spend more time on architecture and user experience - the parts where human creativity matters most,” Szilagyi says.

Navigating the Challenges of AI in Healthcare

For Alex Szilagyi, the biggest challenges in adopting AI have revolved around data privacy, security, and regulatory compliance - all critical in healthcare.

“Healthcare is heavily regulated, so data privacy and compliance were immediate hurdles,” he notes.

Another key challenge has been ensuring that AI models remain explainable and unbiased, especially when decisions can directly impact patient care. As Szilagyi explains, transparency and accountability are essential to maintaining trust in AI-driven healthcare applications.

Advice for Businesses Exploring AI

“Start with a real-life problem, not with AI itself,” says Szilagyi. His advice is simple but powerful: focus on creating “life value” - real-world impact for your customers.

He emphasizes that AI should be treated as a tool, not the goal. Businesses that begin with a clear problem and a strong data strategy will not only save time and resources but also build solutions that truly matter.

The Future of AI in Software Development

Alex Szilagyi believes the next few years will redefine how technology serves both developers and end users. He predicts that generative AI for code and content will soon become the industry standard, dramatically improving productivity.

In healthcare, predictive AI will play a major role in early diagnosis and personalized treatment. At the same time, edge AI will enable models to run directly on medical devices and wearables, enhancing privacy and reducing latency.

And as adoption grows, explainable and ethical AI will no longer be optional. “Regulators will demand it,” Alex notes, highlighting that the next wave of innovation will need to balance intelligence with accountability.

Preparing Teams for the AI Shift

At Life Value, Alex emphasizes that building AI-ready teams isn’t just about learning new tools - it’s about understanding the ecosystem in which they operate. His team invests heavily in AI and data science training, but equally in healthcare-specific knowledge, such as medical regulations and patient data protection.

Each member is encouraged to validate AI outputs critically, design human-AI collaboration flows, and stay proactive about compliance. He explains, 

“In healthcare, you can’t just innovate - you need to ensure every innovation is safe, ethical, and aligned with medical standards.”

The Evolving Role of Human Developers

According to Alex Szilagyi, AI may reshape how software is built, but it won’t replace the human touch that gives technology purpose. “Human creativity and empathy remain irreplaceable,” he says. Developers will increasingly focus on designing intelligent systems, guiding the ethical use of AI, and crafting exceptional user experiences that technology alone can’t achieve.

While AI takes over the repetitive coding and optimization tasks, it’s still humans who set the vision, define intent, and ensure software delivers real-world impact - especially in sensitive fields like healthcare, where trust and responsibility matter most.

How AI Is Redefining Client Expectations

Alex Szilagyi notes a clear shift in what clients demand from IT service providers:

“Clients no longer ask just for working software. They expect intelligent software - solutions that analyse, predict, and adapt in real time.”

While speed to market is still essential, today’s clients also expect data privacy, compliance with regulations like GDPR and HIPAA, and explainable AI to be part of every project from the start. Beyond that, providers are now seen as strategic partners, expected to guide clients through not just technical challenges but also ethical and regulatory decisions that come with adopting AI.