April 22, 2026
There is a version of clinical research that looks fine from the outside. Boxes get ticked. Reports get filed. Audits come and go without incident. And then there is the version where someone actually cares whether the work is right, not just whether it looks right. Love Oshoakpeme belongs firmly in the second category.
She is a Senior Clinical Research Associate with a background in nursing, public health, medical devices, and oncology trials. Oshoakpeme has spent years working in environments where getting something wrong has consequences you can trace directly back to a person. That kind of experience changes how you approach a monitoring visit.
She has a lot on her plate. There is the day job overseeing oncology trials for a Contract Research Organisation. There is an MBA she is working through alongside it. She is also building Trial Intelligence, a platform that aims to make AI tools useful for people working in clinical research.
It Started With a Book
Oshoakpeme grew up in Nigeria. Around age twelve, she got hold of her cousin’s anatomy and physiology textbooks and read them obsessively, late into evenings, because she found them fascinating. She wanted to understand how the body worked.
That curiosity carried her into nursing, but she was still interested in the machinery behind the care. She followed that pull, and it took her to Medtronic.
Operating Rooms and Real Stakes
As a Medical Device Specialist at Medtronic, she was in operating rooms, working directly with surgical teams, training clinicians on devices, and supporting their use during live procedures.
Oshoakpeme learned that the gap between how a technology performs in theory and how it performs in a real clinical setting is a gap that requires human expertise to close.
The Research Side of Medicine
She obtained a Master of Public Health after which she joined a CRO as a Clinical Research Associate and worked her way into a senior role focusing on oncology.
Her job involves site monitoring, source data review, query resolution, regulatory compliance, and keeping studies on schedule. Also mentoring junior CRAs, which she takes seriously. But the thing that sets her apart from many people doing similar work is harder to put in a job description. She has been on the other side of a monitoring visit. She has been the nurse filling in the forms, fielding the questions, managing the pressures of a busy clinical environment while also keeping a trial running. That experience gives her a quality of attention that is difficult to fake and nearly impossible to teach.
Learning the Hard Way, Then the Right Way
She is honest about the fact that the transition from working in Nigeria to navigating clinical research in North America wasn’t smooth. The regulatory environment was different. The documentation expectations were different. Some of it was a matter of learning new processes. Some of it was more unsettling: realising that things she had understood to be standard practice were not, in this context, sufficient.
“It required a lot of learning, and unlearning,” she says. “Especially around processes, compliance expectations, and the level of detail required in documentation.”
What she gained from it was not just updated knowledge. It was a different relationship with process itself. She stopped following rules because they were rules and started understanding why specific standards exist, what they are protecting against, what happens to patient safety and data integrity when they slip. That shift from compliance as a checklist to compliance as genuine comprehension became one of the more durable things she took from that period.
She also became more willing to ask questions. Research environments reward confidence, and confidence can make people reluctant to admit uncertainty. She learned to be more useful than impressive, which turns out to be harder and more valuable.
Trial Intelligence
She has a fairly low tolerance for conversations about AI that stay at the level of potential. The clinical research field has plenty of those conversations. What it has fewer of are tools built by people who actually understand how the work gets done day to day: the monitoring reports, the query workflows, the way time disappears into tasks that are necessary but not intellectually demanding.
Trial Intelligence is her attempt to address that gap. The platform she is building is designed to bring AI into real research workflows in ways that are useful rather than impressive. Reducing the friction on routine tasks. Supporting professionals through complex processes. Freeing up the kind of concentrated attention that clinical research actually requires for the things that automated tools can’t handle.
The MBA she is pursuing runs alongside all of this. She wants to move into leadership eventually, into roles that require both clinical understanding and strategic thinking. The degree will give her the tools to operate at that level when she gets there.
What Comes Next
Within her clinical research career, gene therapy and genomics are where she is pointing her longer-term attention. These are fields where the science is genuinely extraordinary and the research infrastructure is, in many ways, still catching up. The trials are unlike conventional oncology studies in important respects. The regulatory frameworks are evolving. The data is among the most sensitive that medicine produces.
What Actually Drives Her
Ask her what the through-line is across everything she has done, and she does not reach for language about impact or purpose or legacy. She comes back to something more specific: doing the work properly. Not as a standard to perform but as a genuine requirement of what the work is.
“I care deeply about doing work properly,” she says, “not just completing tasks, but making sure the details are right, because they ultimately affect patient safety and study outcomes.”
It is not a complicated philosophy. But it is a consistent one, and in a field where the distance between a rigorous study and a sloppy one can be measured in patient outcomes, consistency of that kind is worth more than it might sound.
Clinical research is changing fast. More data, more technology, more pressure to do more with less time and fewer resources. The professionals who will matter most in that environment are the ones who can hold the human dimension of the work steady while everything else accelerates. Love Oshoakpeme has been preparing for that for longer than the conversation about AI in healthcare has even existed.
About the Author
Lisa Smith is a senior features writer at Harcourt Health Review, covering emerging leaders and innovations in global healthcare and clinical research. She specialises in profiling professionals at the intersection of medicine, technology, and patient impact.