Following her recent webinar, AI Adoption in Healthcare: From Innovation to Implementation, Techparley spoke to Precious Ejiba, Founder and CEO of DB Biomedical, discussing why artificial intelligence has yet to achieve widespread adoption across healthcare despite years of technological advancement.
A biomedical technologist, biotechnologist and data scientist, Ejiba focuses on using AI for preventive healthcare. She argues the main barriers to adoption are not technical, but lie in leadership, trust, governance and the complexity of health systems.
In the conversation, she reflects on her webinar, shares lessons from building digital health tools, and argues that healthcare’s AI future depends on systems thinking — not just algorithms.
During your webinar, one statement immediately caught people’s attention. You said, “AI adoption is not a technology problem. It is a systems problem.” That challenges one of the most common assumptions in healthcare. What led you to that conclusion?
Precious Ejiba: That conclusion came from both research and practical experience.
When people talk about AI adoption, the conversation often centres on algorithms, computing power or model accuracy. Those are important, but they are rarely the reason implementation succeeds or fails.
Healthcare operates as a complex system. Every decision involves clinicians, patients, hospital management, regulators, technology providers and existing clinical workflows. If any one of those components is unprepared, even an outstanding AI solution can struggle to gain traction.
One of the biggest lessons from my work has been that technology does not operate in isolation. Successful innovation depends on understanding the relationships between people, processes and technology. That is why I believe systems thinking has become just as important as technological advancement.
Another point that generated considerable discussion during your webinar was your emphasis on explainable AI. You described it as being essential for building clinical trust. Why do you see explainability as such a critical issue?
Precious Ejiba: Trust is the foundation of healthcare. Unlike many industries, healthcare decisions directly influence patient safety and clinical outcomes.
If an AI system recommends a diagnosis, identifies a patient as high risk or suggests a treatment pathway, healthcare professionals need to understand why that recommendation was made.
Explainable AI provides that transparency. It allows clinicians to interpret recommendations, challenge them where necessary and integrate them into their own professional judgement.
I often say that AI should never ask clinicians to surrender their expertise. Instead, it should strengthen their decision making by providing insights they can understand, evaluate and trust.
Without explainability, even highly accurate systems may struggle to achieve meaningful adoption because confidence is just as important as capability.
You’ve spent years studying healthcare systems while simultaneously building digital health products. Has that combination changed the way you think about innovation?
Precious Ejiba: Very much so. Research allows you to understand why problems exist, while building products forces you to solve those problems in the real world.
One thing I’ve learned is that healthcare professionals are generally open to innovation. What they resist are solutions that create additional complexity or fail to fit naturally into existing clinical practice.
That experience has reinforced my belief that technology should adapt to healthcare rather than expecting healthcare to reorganise itself around technology.
When we design solutions with clinicians instead of simply designing for clinicians, adoption becomes much more achievable.
Leadership featured prominently throughout your webinar. You argued that many organisations focus heavily on technology while underestimating organisational readiness. What do healthcare leaders need to do differently?
Precious Ejiba: Digital transformation is fundamentally a leadership challenge. Technology can enable change, but leaders create the environment where change becomes possible.
That means establishing a clear vision, investing in workforce readiness, developing appropriate governance, encouraging collaboration across disciplines and creating confidence among both clinicians and patients.
I’ve seen organisations purchase sophisticated technologies only to discover that staff training, workflow redesign and governance were treated as afterthoughts.
The most successful organisations understand that implementation begins long before software is installed.
Your work has consistently focused on preventive healthcare. How do you see artificial intelligence changing the way healthcare systems approach prevention over the next decade?
Precious Ejiba: Historically, healthcare has often been reactive. Patients seek care after symptoms develop or disease has already progressed. Artificial intelligence creates an opportunity to move much earlier in that journey.
By combining clinical data, behavioural information and continuous monitoring, AI can identify patterns that indicate increasing risk before serious complications occur. That enables earlier interventions, more personalised support and better long term outcomes.
For me, that is one of the most exciting opportunities in digital health. The greatest impact of AI may not be helping us respond to illness more quickly, but helping us prevent illness more effectively.
If you were advising the chief executive of a hospital beginning its AI journey today, what would be your first recommendation?
Precious Ejiba: Start with the problem rather than the technology. Organisations sometimes become fascinated by what AI can do before clearly defining what they actually need to improve.
The better approach is to identify genuine clinical or operational challenges, involve healthcare professionals from the beginning, establish clear governance, measure outcomes carefully and implement AI incrementally.
Successful adoption is not determined by purchasing the most advanced technology. It is determined by creating the right conditions for innovation to succeed.
Many people predict that AI will eventually replace large parts of healthcare. From your perspective, is that where the future is heading?
Precious Ejiba: I don’t believe the future of healthcare is artificial intelligence replacing human intelligence. I believe it is artificial intelligence amplifying human intelligence.
Healthcare will always require empathy, ethical judgement, communication and clinical experience. Those qualities cannot simply be automated.
The greatest opportunity lies in allowing AI to analyse information at scale while enabling healthcare professionals to spend more time applying their expertise where it matters most: caring for patients.
That partnership between people and technology is where I believe the future of healthcare truly exists.
Finally, after your research, your product development journey and your recent webinar, what message would you leave with healthcare leaders who are still uncertain about embracing AI?
Precious Ejiba: I would encourage them to see AI as an organisational transformation rather than a software implementation. Technology alone will not improve healthcare.
Progress happens when innovation is supported by trusted leadership, responsible governance, explainable systems, engaged clinicians and a clear understanding of how complex healthcare environments actually function.
If we focus on those foundations, I believe artificial intelligence can help healthcare become more preventive, more personalised and ultimately more human centred.
The question is no longer whether AI belongs in healthcare. The real question is whether we are building healthcare systems that are ready to use it responsibly.
Editor’s Note
Precious Ejiba is the Founder and Chief Executive Officer of DB Biomedical, a UK and Nigerian health technology company focused on digital innovation in preventive healthcare. Her recent webinar explored the organisational, technological and governance challenges influencing AI adoption across healthcare systems and highlighted the role of systems thinking in enabling responsible implementation.
