By Iroyin Yoruba Television
The Federal Government has moved to facilitate clinical testing of a Nigerian-developed artificial intelligence technology that could dramatically reduce the time required for magnetic resonance imaging scans, potentially expanding the effective capacity of existing diagnostic equipment across the country.
The technology, known as GenScan AI, was presented to government officials, healthcare stakeholders, scientists and technology experts in Abuja on Monday, October 5, following its selection as the winner of the 2026 Nigeria Prize for Science and Innovation.
Developed by Nigerian artificial intelligence researcher Mary-Brenda Akoda, GenScan AI uses an AI-based image reconstruction system designed to reduce MRI acquisition time by as much as 90 per cent without requiring hospitals to purchase new MRI machines.
Education Minister Dr Tunji Alausa said the Federal Government would facilitate the innovator's engagement with federal teaching hospitals and Federal Medical Centres to enable clinical pilots of the technology in Nigeria.
The development is significant for Nigeria's healthcare system because access to advanced medical imaging remains constrained by the limited availability and high cost of MRI equipment.
If clinical testing confirms the technology's reported performance and safety, it could allow compatible MRI scanners to examine substantially more patients within the same operating period.
However, the technology is not yet being presented as an established replacement for conventional MRI procedures. Further clinical assessment is required to determine how it performs across different patient groups, medical conditions and healthcare environments.
AKODA'S TECHNOLOGY RECEIVES GOVERNMENT BACKING
Alausa announced the Federal Government's support during the public presentation of GenScan AI in Abuja.
The minister described the innovation as an example of research that could move beyond academic work and address a practical healthcare challenge.
He said he would facilitate contact between Akoda and the Federal Ministry of Health and help connect the innovator with the leadership of federal teaching hospitals.
The proposed clinical pilots are important because technologies used for medical diagnosis require careful testing before they can be incorporated into routine patient care.
A system that reconstructs medical images must produce results that clinicians can trust. Its performance must be evaluated against established diagnostic standards, and medical professionals must understand how to use and interpret its output.
The planned engagement with teaching hospitals and Federal Medical Centres could therefore provide an important bridge between laboratory research and real-world clinical application.
HOW GENSCAN AI WORKS
GenScan AI is built around the C-MORE algorithm, an artificial intelligence system designed to reconstruct MRI images from fewer data acquisitions.
Instead of requiring a scanner to collect the full amount of information traditionally needed for an examination, the system uses AI to reconstruct the image.
According to information presented at Monday's event, the technology could potentially reduce the acquisition time of an MRI examination by up to 90 per cent.
In practical examples cited by Akoda, a scan that normally takes about 20 minutes could potentially be reduced to approximately two minutes, while a one-hour scan could potentially take about six minutes.
These figures describe the potential of the technology and will need to be assessed through appropriate clinical validation.
The central attraction is that the proposed reduction in scanning time does not depend on hospitals purchasing entirely new MRI machines.
Instead, the innovation is designed to work with existing compatible equipment.
WHY MRI SCAN TIME MATTERS
MRI is an important diagnostic technology used to examine soft tissues and structures inside the body.
Doctors may request MRI examinations when investigating conditions affecting the brain, spine, joints, organs and other parts of the body.
However, MRI examinations can take considerable time.
Patients may need to remain still for extended periods while the scanner acquires the required images.
Movement during an examination can reduce image quality and may sometimes require additional scans.
This can be particularly difficult for children, elderly patients, people experiencing severe pain and patients who have difficulty remaining still.
Long scanning times also affect the number of examinations a facility can conduct during a working day.
Reducing the time required for each examination could therefore increase the number of patients that an existing scanner can potentially serve.
NIGERIA'S MRI CAPACITY CHALLENGE
The potential significance of GenScan AI is linked to the wider challenge of access to advanced diagnostic equipment in Nigeria.
MRI scanners are expensive medical devices, and healthcare institutions must also meet the costs of installation, maintenance, electricity, specialised personnel and other operational requirements.
Akoda said high-quality MRI scanners can cost between $1.5 million and $3.5 million or more depending on the model and specifications.
For healthcare providers operating under financial constraints, purchasing additional machines to meet rising demand can therefore be difficult.
A technology capable of increasing the productivity of existing machines could offer an alternative way of expanding effective diagnostic capacity.
Rather than immediately adding more hardware, hospitals could potentially increase the number of patients examined by each compatible scanner.
That could help reduce waiting lists and make diagnostic services more accessible.
THE POTENTIAL BENEFIT FOR PATIENTS
For patients, shorter MRI examinations could provide several potential advantages.
A patient who currently has to wait weeks for an available appointment could potentially obtain an examination sooner if facilities are able to process more patients.
Shorter examinations could also make the experience less difficult for patients who struggle with prolonged periods inside an MRI scanner.
Reducing the duration of an examination could be particularly useful where patient movement is a problem.
If a patient moves during a scan, the resulting images may be affected, potentially requiring additional imaging.
A shorter procedure could reduce the time during which patients have to remain completely still.
However, these benefits will depend on clinical validation and the ability of hospitals to integrate the technology properly into their existing workflows.
THE TECHNOLOGY DOES NOT ADD NEW MRI MACHINES
One of the key features of GenScan AI is that it seeks to improve the use of existing MRI infrastructure rather than requiring new scanners.
This distinction is important.
Nigeria's diagnostic challenge is not simply a question of how many machines exist. Facilities also need to use those machines efficiently.
A scanner that can only examine a limited number of patients each day because examinations take a long time may not meet demand even when it is functioning properly.
If GenScan AI can safely reduce scan acquisition times while maintaining appropriate diagnostic quality, the same scanner could potentially serve substantially more patients.
That could increase the effective capacity of existing healthcare infrastructure.
CLINICAL TESTING WILL BE CRITICAL
Despite the excitement surrounding the innovation, clinical validation remains essential.
Medical AI systems must be tested under real clinical conditions before they can be relied upon for patient care.
Researchers and healthcare professionals need to establish whether the reconstructed images consistently provide the quality required for different diagnostic purposes.
They also need to understand how the technology performs across different MRI machines, imaging protocols and patient populations.
A technology that works effectively under controlled research conditions must still demonstrate reliability in hospitals where equipment, staffing and patient characteristics can vary.
This is why the proposed clinical pilots are an important next step.
GOVERNMENT SEEKS TEACHING HOSPITAL PARTNERSHIPS
The Federal Government plans to facilitate partnerships between Akoda and federal teaching hospitals and medical centres.
Teaching hospitals are particularly relevant because they combine patient care, medical education and research.
They can provide access to clinicians and patients while maintaining research structures capable of evaluating new medical technologies.
The involvement of specialists will also allow the technology to be assessed from the perspective of radiologists and other healthcare professionals who routinely interpret medical images.
Their feedback could help determine where the technology is most useful and what modifications may be required before wider deployment.
FROM RESEARCH TO HEALTHCARE
The government's support also reflects a broader policy objective of turning Nigerian research into practical solutions.
Nigeria has universities, researchers and technology professionals developing innovations in several fields.
One of the longstanding challenges, however, has been moving promising research from laboratories into products and services that people can actually use.
Alausa said GenScan AI represented the kind of innovation that should move beyond research and demonstrate practical value.
The Federal Government's proposed support for clinical pilots could therefore become an example of how research institutions, innovators, government and healthcare providers can work together.
THE SIGNIFICANCE OF THE $100,000 PRIZE
Akoda's innovation emerged as the winner of the 2026 Nigeria Prize for Science and Innovation, sponsored by Nigeria LNG Limited.
The competition attracted 237 entries under a theme focused on artificial intelligence, information and communication technology and digital technologies for development.
The prize carries an award of $100,000.
The 2026 result was particularly notable because the previous edition ended without a winner after the entries were judged not to have met the required standard.
Akoda's selection therefore represents a return to recognising a winning innovation after the previous year's decision.
She is also the first woman and first millennial to win the prize as an individual recipient.
AKODA'S SCIENTIFIC BACKGROUND
Akoda has an academic background in computer science and artificial intelligence.
She earned a first-class degree in Computer Science from Goldsmiths, University of London, and later obtained a Master of Research with distinction in Artificial Intelligence and Machine Learning from Imperial College London.
She has also worked as a software engineer and AI research scientist.
Her work on GenScan AI combines artificial intelligence with medical imaging, bringing together two areas that are increasingly influencing healthcare globally.
The recognition from the Nigeria Prize for Science and Innovation places the technology among Nigerian research projects receiving national attention for their potential practical impact.
INTERNATIONAL RESEARCH AND CLINICAL LINKS
Akoda said the technology has already attracted international research and clinical interest.
The innovation has been supported by organisations including Innovate UK, UK Research and Innovation and the NHS Clinical Entrepreneur Programme.
She also indicated that clinical partnerships have been established in several countries, including Nigeria.
These connections could provide additional opportunities for testing and development.
However, the immediate focus for Nigeria is to determine how the technology can safely and effectively operate within the country's healthcare system.
Local clinical testing will be important because healthcare environments differ considerably.
POTENTIAL IMPACT ON HEALTHCARE COSTS
If GenScan AI proves effective, its economic implications could extend beyond shorter waiting times.
MRI machines represent substantial capital investments for hospitals.
If healthcare providers can increase the number of examinations performed using existing scanners, they may be able to expand diagnostic capacity without immediately purchasing additional equipment.
That could make better use of existing investments.
Patients could also potentially benefit if increased capacity reduces waiting times and makes appointments more readily available.
However, the technology itself will still require appropriate software infrastructure, maintenance, cybersecurity safeguards, trained personnel and regulatory oversight.
Its potential cost savings therefore need to be considered alongside the expenses involved in safe deployment.
PATIENT SAFETY MUST COME FIRST
Medical innovation must ultimately be judged by its impact on patients.
Speed alone cannot determine whether an imaging technology is suitable for clinical use.
The reconstructed images must be sufficiently accurate for the intended diagnostic purpose.
Healthcare professionals must be able to identify abnormalities reliably.
The technology must also be tested against potential risks, including cases where image reconstruction might create misleading information.
These considerations make clinical validation essential before widespread adoption.
The Federal Government's decision to support pilot programmes therefore provides an opportunity to establish evidence before deployment at scale.
AI AND THE FUTURE OF MEDICAL IMAGING
GenScan AI reflects a wider global trend toward using artificial intelligence to improve medical imaging.
AI is increasingly being explored for image reconstruction, analysis, triage and workflow optimisation.
The potential benefits include faster processing, improved resource utilisation and assistance to healthcare professionals.
For countries where advanced medical equipment is expensive or limited, technologies that increase the efficiency of existing infrastructure can be particularly valuable.
Nigeria's growing technology ecosystem could potentially produce more innovations addressing similar healthcare challenges.
The government's response to GenScan AI may therefore influence how future Nigerian health technologies move from research into clinical practice.
WHAT HAPPENS NEXT
The immediate next step is to establish clinical pilot programmes involving teaching hospitals and Federal Medical Centres.
The Federal Ministry of Education is expected to help facilitate the connections needed for these trials, while the health sector will need to participate in evaluating the technology.
Clinical experts will have to determine the appropriate areas for testing and establish the standards against which the AI system will be assessed.
If the pilots produce positive results, further regulatory, technical and commercial steps could follow.
The technology could then potentially be introduced to additional healthcare facilities, subject to the necessary approvals and safeguards.
A POTENTIAL BOOST FOR EXISTING INFRASTRUCTURE
Nigeria's healthcare system faces a difficult combination of limited specialist equipment, high patient demand and financial constraints.
GenScan AI does not solve all of these problems.
It does, however, target one specific bottleneck: the amount of time required to obtain MRI images.
If the technology can safely shorten examinations while preserving diagnostic quality, it could increase the effective capacity of existing scanners.
That could be particularly valuable in major hospitals and diagnostic centres where patients currently face long waiting periods.
The innovation's value will ultimately depend on evidence from clinical use rather than the headline figure attached to its potential speed.
CONCLUSION
The Federal Government's decision to support clinical testing of GenScan AI marks a significant development in Nigeria's effort to connect artificial intelligence research with practical healthcare needs.
The technology, developed by Mary-Brenda Akoda, could potentially reduce MRI acquisition times by up to 90 per cent without requiring hospitals to purchase new scanners.
For a country facing constraints in access to advanced diagnostic equipment, the potential to increase the productivity of existing MRI machines could be significant.
But the technology must now pass through the crucial stage of clinical validation.
Teaching hospitals and Federal Medical Centres will need to determine whether it can consistently produce images of the quality required for medical diagnosis and whether it can operate safely across different clinical environments.
If those tests are successful, GenScan AI could become an example of how Nigerian research can contribute directly to solving challenges within the country's healthcare system.
The immediate opportunity is therefore not simply to celebrate the innovation but to test it rigorously, establish its clinical value and create a pathway for responsible deployment.
For patients, the potential reward is substantial: shorter diagnostic procedures, reduced waiting times and greater use of existing MRI infrastructure.
For Nigeria's health and technology sectors, the development offers another important lesson — innovative ideas can have their greatest impact when scientific research is connected to real clinical needs and supported through the difficult journey from the laboratory to the hospital.