Medicine has always demanded decisions made under pressure, with the information available in that moment. That is the nature of the work. What is changing now is how much better that information can become.
Artificial intelligence can now process what no human mind could hold at once. Millions of cases, hidden patterns, real-time analysis that improves as it learns. The organizations doing this well are not simply delivering software to hospitals. They are redefining what it means to make a clinical decision.
As CEO of Artificial Intelligence Expert (AIE), Alexandru Floares brings something uncommon to the role. A neurologist by training, his career took him somewhere most clinicians never go. He spent years working at the edge of what AI could do in medicine, well before it attracted the attention it does today. That path gave him something difficult to manufacture: a clinical mind that also understands the technology from the inside. AIE reflects that. The company measures itself not by the sophistication of its systems, but by what those systems actually change for doctors and patients.
Let’s dive into this story of vision, science, and leadership shaping the future of AI-driven healthcare and patient-centered innovation!
A Vision Rooted in Real-Time Intelligence
At the core of AIE’s journey is a vision that is both bold and practical. As Floares explains, “My vision is straightforward but ambitious: every clinical decision should be informed by the best available data, analyzed by the most capable AI, and delivered at the point of care — in real time.” This idea is not just a statement. It guides everything the company builds and works toward.
AIE brings together three important areas. The first is multimodal biomedical data, including genomics, proteomics, metabolomics, medical imaging, and structured clinical records. The second is advanced AI and machine learning. The third is digital twin technology. Instead of working on these separately, the company combines them into one system. This makes it easier to answer real clinical questions in a clear and useful way.
Unlike many companies that begin with technology, AIE starts with the medical problem. It could be early cancer detection, predicting how a patient might respond to a specific antidepressant, or tracking a chronic condition in real time. The focus always stays on the clinical need. Once the problem is clear, the AI is built to solve it. This keeps the work practical and directly useful in real-world settings.
This approach can already be seen in the company’s key projects. The i-Biomarker CaDx platform, which is internationally patent-pending, focuses on early detection of multiple cancers. It uses circulating miRNA panels and explainable AI, and has achieved 97 to 99 percent accuracy, especially in early stages. This is higher than many existing commercial solutions. Another important project is OPADE, an EU-funded clinical decision support system for depression. It works through a three-layer structure. It turns clinical guidelines into workflows, uses generative AI for complex cases, and helps doctors make decisions when guidelines do not clearly align.
Beyond diagnostics, AIE is also working on DTRIP4H, which focuses on digital twin technology for personalized health monitoring. At the same time, MiWear is exploring metabolic monitoring using wearable biosensors and AI. Together, these efforts show a complete approach that covers both early detection and ongoing health management.
Floares is clear about one thing. AI is not here to replace doctors. It is here to support them. By finding patterns in large and complex datasets, AI helps doctors make faster decisions and better-informed choices.
Origins in Clinical Reality
This vision comes from real clinical experience. During his time as a neurologist in Romania, Floares observed a key challenge in everyday practice. As he recalls, “Every day in the clinic, I watched brilliant physicians make decisions based on incomplete information — limited imaging, scarce lab data, and guidelines built on population averages rather than individual biology.” This realization shaped his future path.
It showed a clear gap in healthcare. There was a growing amount of biomedical data, but doctors could not fully use it. Data was increasing at a rapid pace, yet without the right tools, much of it remained unused.
His PhD in biophysics introduced him to computational modeling. This helped him understand how AI could be applied in medicine. Acting on this insight, he founded the first Artificial Intelligence Department at the Oncology Institute of Cluj-Napoca in 2000. This was long before AI became widely used in healthcare.
This early step was important. It set the foundation for a career focused on connecting data with real clinical use. AIE was built with this same goal. The aim is to make sure biomedical data is actively used to improve patient care.
Leadership Guided by Precision and Patience
Leading in a fast-changing field like healthcare AI requires a clear and steady approach. Floares follows a principle he learned from Roman philosophy. “I lead with a principle I borrowed from the Romans: festina lente — make haste slowly.” In a space where new technologies appear quickly, this mindset helps maintain focus.
Instead of following every trend, AIE stays focused on what truly matters. The team is carefully built and highly skilled. It includes experts in medicine, data science, and software engineering. This mix allows them to build solutions that are both scientifically strong and technically reliable.
Transparency is also a key part of leadership. The company works in EU-funded collaborations involving close to 100 partner organizations across Europe. This includes large initiatives such as the 53-partner UNCAN-Connect cancer data federation. These collaborations involve research institutions, university hospitals, SMEs, and industrial partners across 19 countries. In such an environment, trust is essential. It is not just a value. It is how the organization operates.
Innovation Anchored in Rigor
In healthcare, innovation must be handled with care. As Floares says, “Innovation without rigor is dangerous in healthcare — you are dealing with people’s lives.” This belief shapes every part of AIE’s work.
Every AI model must be explainable, reproducible, and supported by validated data. Tools like SHAP analysis are used as a standard, especially in diagnostic and predictive systems. In the i-Biomarker CaDx platform, the system not only gives a probability of cancer. It also shows which biomarkers influenced the result. This helps doctors understand and question the outcome if needed.
Innovation is supported through the company’s involvement in EU-funded research projects such as OPADE, DTRIP4H, MiWear, and UNCAN-Connect. Each project explores a different area. Each also provides access to advanced clinical data, international collaboration, and structured validation processes required by Horizon Europe programs.
Strategic Clarity in a Complex Data Landscape
Biomedical data is complex in many ways. As Floares explains, “Biomedical data is not just big — it is high-dimensional, multimodal, and often noisy.” This makes it difficult to handle without proper context.
AIE begins with biological and clinical understanding. Instead of simplifying data too much, the company keeps its full complexity when needed. In OPADE, they use the full 17-item Hamilton Depression Rating Scale instead of reducing it to a single score. They apply Euclidean distance-based methods to better capture treatment response.
To stay focused, the company works within six defined service pillars. These include predictive biomarker analytics, clinical decision support, digital twin modeling, wearable AI and metabolic monitoring, medical data engineering, and AI consulting for healthcare organizations. Every project must fit into at least one of these areas. If it does not fit, it is not pursued.
Designing for Real-World Impact
For AIE, real-world use is critical. Floares clearly states, “If a system cannot integrate into an actual clinical workflow, it stays in the lab.” This ensures that their work has practical value.
The company works closely with clinicians, including its co-founder and Chief Medical Officer, Dr. Carmen Floares, who is an experienced oncologist. This ensures that all systems meet real clinical needs.
The OPADE system is designed with three layers to reflect real clinical thinking. The first layer handles simple cases using guidelines. The second layer uses generative AI for complex cases with co-morbidities. The third layer supports decisions when guidelines conflict or when patient preferences must be considered.
Navigating Challenges with Resilience
One of the biggest challenges in healthcare AI is time. Floares explains, “In healthcare AI, the distance between a promising research result and a product that can be used on a real patient is measured in years, not months.” This includes regulatory approval, clinical validation, data privacy compliance, and intellectual property protection.
As a European SME, AIE also faces competition from larger global companies. However, it uses its strengths such as agility, deep expertise, and hybrid team skills to stay competitive. Participation in many European partnerships provides access to clinical data, validation environments, and domain expertise that would otherwise be very costly to build independently.
Another challenge is protecting intellectual property, especially within multi-partner EU consortia. In such environments, multiple organizations may have access rights to both foreground and background intellectual property. This requires careful strategy and constant attention.
Cultivating Talent for a Hybrid Future
The company looks for people who understand both AI and healthcare. These profiles are rare, so recruitment is global. The team includes members from Romania, Greece, and Pakistan, working in a distributed model supported by EU research collaborations.
Retention is driven by purpose. Working on projects that can help detect cancer earlier or improve patient outcomes keeps people motivated. Continuous learning is also encouraged. Team members have access to the latest foundation models, agentic AI frameworks, and development tools. In a field that evolves quickly, this access is essential.
Embedding Ethics into Engineering
Ethics is built into every system. As Floares says, “Ethics in healthcare AI is not an afterthought or a compliance checkbox — it is an engineering requirement.”
All systems are designed to be transparent and explainable. They follow strict data protection standards such as GDPR and HIPAA. Participation in EU Horizon Europe projects adds another layer of ethical oversight. These projects require ethics approvals, data management plans, and structured governance.
Accountability is also personal. As both a physician and a CEO, Floares recognizes that these tools will influence decisions about real patients.
Toward a Digital Triplet Future
Looking ahead, Floares shares his long-term vision. “In the next decade, I believe we will see the emergence of what is called the ‘Digital Triplet’ in healthcare — going beyond the digital twin.” This concept adds a cognitive and agentic AI layer that can reason about patient data, simulate treatment scenarios, and support decisions in real time.
This vision connects closely with ongoing work in DTRIP4H and future EIC Pathfinder proposals. At the same time, multi-cancer early detection through liquid biopsy is expected to become a routine part of preventive medicine within five to seven years. AIE aims to play an important role in this transformation by focusing on validation, regulatory approval, and accessibility.
A Legacy Defined by Impact
As the company prepares to launch its biomedical data analysis services in 2026, it aims to bring its AI capabilities to pharmaceutical companies, clinical research organizations, and healthcare institutions worldwide. These services are built on six core pillars and are designed for both European and American markets with full regulatory compliance.
In the end, the goal is not just growth. It is impact. Floares wants to show that a focused and expert team can compete globally by combining clinical knowledge, scientific rigor, and strong engineering.
AIE’s journey shows that success is not only about size. It is about clarity, purpose, and meaningful innovation.
