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A Nigerian Artificial Intelligence (AI) researcher, Mary-Brenda Akoda, has emerged winner of the 2026 Nigeria Prize for Science and Innovation, organised by Nigerian LNG Limited (NLNG), after beating 236 other entries in a competition that attracted 237 submissions.
She developed an imaging technology capable of reducing Magnetic Resonance Imaging (MRI) scan time by up to 90 per cent, potentially enabling hospitals to examine thousands more patients without purchasing additional scanners.
Akoda’s innovation, GenScan AI, earned her the $100,000 prize money attached to the award.
She explained that the technology was designed to work with existing MRI machines and can reduce a 20-minute scan to about two minutes and a one-hour scan to approximately six minutes.
She disclosed this while presenting the innovation to health, science and technology experts, policymakers and representatives of medical institutions in Abuja on Monday.
Akoda noted that MRI scans can be particularly challenging for patients who are in pain, anxious, elderly or unable to remain still for extended periods. “Patient movement can result in blurred images, thus requiring repeated scan sessions and, in some cases, sedation,” she explained.
She further said the problem was compounded by the high cost and limited availability of MRI machines, with high-quality scanners costing between $1.5 million and $3.5 million or more, depending on the model.
“Every second counts,” Akoda said, recalling the case of a patient who required an urgent scan but faced a long queue, deteriorated and subsequently died.
She said such experiences motivated the development of GenScan AI, which uses AI to reconstruct diagnostic-quality MRI images from substantially fewer acquired data points.
According to her, the technology achieved up to 99.7 per cent similarity to conventional MRI scans, despite the 90 per cent reduction in scan time.
A more important test, she said, involved a blinded assessment by a consultant radiologist at Buckinghamshire NHS Hospital in the United Kingdom, who was presented with conventional and GenScan AI images without knowing which was which.
“The consultant radiologist scored the GenMRI images as diagnostically equivalent to the conventional scan in all the cases that were assessed,” Akoda said.
She added that the technology had so far resulted in two peer-reviewed publications and two conference presentations, while receiving support from Innovate UK, UK Research and Innovation and the NHS Clinical Entrepreneur Programme.
Akoda said clinical partners had also been secured in the UK, United States, Kenya and Nigeria, with a Nigerian diagnostic centre supporting a planned clinical pilot.
She said the next steps were regulatory submission and clinical trials, with commercial deployment targeted for the second half of 2027. “The potential impact is substantial.”
Akoda said a health economics case study found that even at a four-fold increase in scanning speed, the technology could enable more than 40,000 additional patients to be scanned annually.
Speaking at the event, Dr Sophia Horsfall, Executive Director, External Affairs, NLNG, said effective treatment begins with accurate diagnosis, stressing the importance of diagnostic imaging in helping clinicians make critical decisions.
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She said the prize was designed not merely to recognise research, but to encourage innovations capable of addressing real problems and delivering practical benefits to society.
Chairman of the Advisory Board of the Nigeria Prize for Science and Innovation, Prof Barth Nnaji, said the 2025 competition produced no winner because none of the entries met the rigorous standard required for the prize.
He said the AI, ICT and digital technology theme was repeated, eventually producing Akoda’s innovation as the 2026 winner.
Prof Nnaji stressed that the competition was international and that the award was based on merit rather than nationality.
Minister of Education, Dr Tunji Alausa, in his remarks, described GenScan AI as an example of scientific research being translated into solutions to real human problems.
He said Akoda’s achievement demonstrated that Nigeria’s education system could produce innovators capable of competing globally.
Alausa urged Akoda to protect the intellectual property associated with the innovation and said the Federal Ministry of Education would facilitate access to teaching hospitals and Federal Medical Centres for her planned Nigerian pilots.
He also said the Ministry of Health should be brought into the process to support the clinical deployment of the technology.
Representing the Minister of Innovation, Science and Technology, Dr Kingsley Tochukwu Uduh, SAN, the Director-General/Chief Executive Officer of the National Board for Technology Incubation, Dr Kazeem Kolawole Raji, said the government was committed to moving innovations from research laboratories to commercial applications.
Raji said NBTI’s mandate was to support technology incubation, innovation and commercialisation, adding that the government was creating platforms to connect Nigerian innovators with investors.
He said the agency’s NextGen Innovation Challenge attracted more than 5,000 entries this year and used a technology-readiness assessment framework to screen innovations.
For Akoda, however, the immediate priority is to translate GenScan AI from an award-winning innovation into a clinically approved technology capable of helping hospitals tackle MRI backlogs.
Her message to diagnostic centres, health institutions and potential funders was direct: participate in clinical studies, support pilot programmes and help accelerate the deployment of faster MRI across Nigeria.
The significance of the innovation, as Nnaji put it, goes beyond Nigeria. With MRI capacity limited, equipment expensive and patients often waiting for diagnostic imaging, GenScan AI represents an attempt to increase the productivity of existing infrastructure rather than simply adding more expensive machines.
If its clinical pilots and regulatory process confirm the results already reported by Akoda, the technology could offer hospitals a new way of expanding access to MRI diagnosis while reducing the amount of time patients spend inside scanners.
