By Simpson Global Media News Desk
A Nigerian-developed artificial intelligence technology designed to dramatically shorten magnetic resonance imaging, or MRI, scan times has moved closer to potential real-world application after its developer presented the innovation to health, science and technology stakeholders in Abuja.
Known as GenScan AI, the technology was developed by Nigerian artificial intelligence researcher Mary-Brenda Akoda and emerged as the winning entry of the 2026 Nigeria Prize for Science and Innovation, sponsored by Nigeria LNG Limited.
According to information presented by NLNG and the prize organisers, GenScan AI uses an algorithm known as C-MORE to reconstruct MRI images through a one-step framework and has the potential to reduce MRI scan acquisition times by as much as 90 per cent while maintaining or potentially improving image quality. Importantly, the system is designed to work with existing MRI scanners rather than requiring hospitals to purchase new machines.
The technology is attracting attention because Nigeria’s healthcare system faces limitations in access to advanced diagnostic equipment, while MRI examinations can be lengthy and contribute to patient waiting lists.
GenScan AI does not itself increase the physical number of MRI machines in hospitals. Instead, its proposed value lies in increasing the amount of diagnostic work that compatible existing machines could potentially handle within the same period.
That distinction is important.
The innovation is still moving through the process required to translate research and technological development into routine clinical use. Its presentation to stakeholders is therefore an opportunity to examine its technical capabilities, safety requirements, clinical validation and pathway towards deployment rather than a declaration that every MRI facility in Nigeria can immediately install and use the technology.
A TECHNOLOGY BUILT AROUND AN EXISTING BOTTLENECK
MRI is an important diagnostic technology because it can produce detailed images of structures inside the human body without using ionising radiation.
It is used in the investigation and management of numerous medical conditions, including neurological, musculoskeletal, cardiovascular and oncological problems.
But MRI examinations can take considerable time.
The duration varies according to the body part being examined, the protocol being used, the machine and the clinical question being investigated.
Long examinations can create scheduling pressures for hospitals.
Where demand is high and the number of available machines is limited, patients may face extended waiting periods.
GenScan AI is designed to address one part of that problem: the amount of time required to acquire and reconstruct MRI images.
The technology's developers say it can substantially reduce the time needed for compatible scans while preserving image quality.
The company's own description says its GenMRI software can reduce a 20-minute scan to as little as two minutes under the technology's intended operating conditions. Those figures are claims made by the developer and should be distinguished from independent clinical validation across different hospitals, scanners and patient populations.
The broader proposition is straightforward.
If an existing scanner can safely produce clinically useful images in less time, the same physical machine could potentially be used for more patients during the operating day.
That could improve equipment utilisation without requiring an immediate capital investment in additional MRI hardware.
HOW GENSCAN AI WORKS
The core technology is based on artificial intelligence-assisted image reconstruction.
According to NLNG and the NPSI Advisory Board, the C-MORE algorithm uses a one-step framework to reconstruct MRI images. The intended result is faster acquisition and reconstruction while maintaining or improving the quality required for diagnostic interpretation.
In conventional MRI, the scanner collects signals that are processed to create images.
The length of an examination can depend on how much information is required and the imaging sequence being used.
AI-based reconstruction approaches seek to reduce the amount of scanning required while using computational techniques to reconstruct an image from the information collected.
This is an increasingly important area of medical technology.
The challenge is not simply producing an image quickly.
A medical image must contain sufficient information for a qualified clinician or radiologist to make an appropriate assessment.
An image that is produced faster but loses clinically relevant information would not provide the intended benefit.
That is why the claims surrounding GenScan AI focus not only on speed but also on maintaining or potentially improving image quality.
FROM INNOVATION TO CLINICAL APPLICATION
The presentation in Abuja is significant because it moves the conversation beyond the announcement of an award.
The event brought together stakeholders from healthcare, science, technology, education and medical institutions to examine the innovation and what would be required for practical application.
Sophia Horsfall, NLNG’s General Manager for External Relations and Sustainable Development, said the company wanted scientific research to produce practical benefits rather than remain at the level of recognition.
Professor Barth Nnaji, chairman of the NPSI Advisory Board, similarly said engagement with clinicians and other stakeholders would be important in determining how the technology could be used safely and effectively.
That point is crucial.
An AI system intended for medical imaging has to satisfy requirements that are different from those applying to a general consumer application.
It has to perform reliably.
Its output has to be clinically interpretable.
Its behaviour must be evaluated across appropriate cases.
The system must also be integrated into hospital workflows without introducing new risks.
WHY THE 90 PER CENT FIGURE NEEDS CONTEXT
The claim of up to a 90 per cent reduction in MRI scan time is the headline figure attached to GenScan AI.
It is also the figure that requires the most careful interpretation.
The phrase “up to” matters.
It does not mean that every MRI examination will automatically become 90 per cent shorter.
MRI protocols vary.
Different body parts require different sequences.
Patients have different clinical needs.
Machines have different configurations.
Hospitals also operate different workflows.
Consequently, a technology capable of achieving a very large reduction under particular conditions may deliver different results under other conditions.
The developer says its system can turn a 20-minute scan into about two minutes, and a one-hour examination into roughly six minutes. Those figures illustrate the technology's intended potential, but they should not be interpreted as a guaranteed result for every patient or MRI protocol.
Clinical evaluation is therefore essential.
THE IMPORTANCE OF IMAGE QUALITY
Speed alone does not determine the value of an imaging technology.
Radiologists require images that contain sufficient detail to identify abnormalities and make appropriate clinical assessments.
A technology that produces faster but unreliable images could create new problems.
GenScan AI's proposition is consequently based on two elements: faster scanning and image quality.
NLNG said the innovation has the potential to reduce scan acquisition time while maintaining or improving image quality.
The next stage will require specialists to establish how consistently those claims hold under real clinical conditions.
That can involve testing across different MRI machines, different protocols and different patient groups.
It can also involve assessing whether the AI-generated reconstructions perform adequately in the specific diagnostic situations for which they are intended.
A SOLUTION TO EQUIPMENT LIMITATIONS — NOT A REPLACEMENT FOR MACHINES
Nigeria's MRI capacity cannot be expanded simply through software.
Hospitals still need MRI machines.
They need trained radiographers and radiologists.
They need electricity, cooling, maintenance, compatible infrastructure and reliable supply chains.
Patients still need access to facilities.
GenScan AI therefore should be understood as a potential efficiency technology rather than a replacement for physical healthcare infrastructure.
If successfully validated and deployed, it could allow existing scanners to serve more patients.
But it cannot eliminate the need for additional scanners where demand exceeds capacity even after efficiency improvements.
That distinction matters in planning Nigeria's healthcare technology infrastructure.
WHY EXISTING HARDWARE MATTERS
The fact that GenScan AI is designed to work without new MRI hardware is one of its potentially significant features.
MRI machines can require substantial capital investment.
Hospitals also need appropriate facilities and technical support to install and maintain them.
For health systems operating under financial constraints, increasing the productivity of machines already in service could be attractive.
Instead of relying only on the purchase of more equipment, healthcare providers could potentially combine infrastructure expansion with software-based efficiency improvements.
The exact financial benefit would depend on licensing, installation, validation, maintenance, training and other costs associated with deployment.
Those details will have to be established as the technology moves toward commercial and clinical application.
THE NIGERIA PRIZE FOR SCIENCE AND INNOVATION
GenScan AI was selected as the winning entry for the 2026 Nigeria Prize for Science and Innovation.
The competition attracted 237 submissions, making it the largest number of entries recorded for the prize, according to reports on the award.
Akoda was selected after an evaluation process that considered scientific innovation, technical depth, developmental impact, scalability and practical application.
The prize carries an award of US$100,000.
The recognition is significant for the technology because it provides visibility beyond the startup and research community.
It places GenScan AI within a national conversation about how Nigerian science and engineering can generate solutions for local problems.
The prize also illustrates the increasing intersection between artificial intelligence and traditional scientific fields.
AI is no longer limited to software development or consumer applications.
It is increasingly being applied to medical imaging, agriculture, manufacturing, finance, education and scientific research.
AKODA'S CAREER IN AI
Akoda's background also reflects the increasingly international character of Nigeria's technology ecosystem.
BusinessDay reported that she studied Computer Science at Goldsmiths, University of London, and completed a Master of Research with Distinction in Artificial Intelligence and Machine Learning at Imperial College London, where she was a Google DeepMind Scholar.
The publication also reported that she had worked as a software engineer and AI research scientist at Microsoft, including with the company's Mixed Reality and AI Research Lab in Cambridge.
Her career illustrates how Nigerian technology talent can combine international research and industry experience with problems that are particularly relevant to African healthcare.
That combination is important to the wider development of Nigeria's technology sector.
The country does not only need consumers of imported technology.
It also needs researchers and companies capable of developing intellectual property and products that can address domestic challenges.
FROM NIGERIAN PROBLEM TO GLOBAL MARKET
The potential market for medical-imaging AI extends beyond Nigeria.
MRI is used worldwide.
If GenScan AI can demonstrate reliable performance across multiple machine types and clinical settings, the technology could potentially be relevant to healthcare systems outside Nigeria.
The developer describes the product as being intended for medical imaging systems internationally.
But international expansion would bring additional regulatory requirements.
Medical technology is subject to different approval and safety frameworks in different jurisdictions.
A technology that has been developed in Nigeria may need additional validation before it can be deployed in another country.
That process can take time.
The opportunity is nevertheless significant because software-based medical technology can potentially scale across borders more readily than physical infrastructure, provided the necessary technical and regulatory requirements are met.
THE ROLE OF CLINICIANS
Technology developers cannot determine clinical usefulness alone.
Radiologists, radiographers, clinicians, hospital administrators and medical-device specialists all have roles to play in evaluating a system such as GenScan AI.
Radiologists are particularly important because they ultimately have to interpret the images.
They can help determine whether the reconstructed images provide the necessary diagnostic information.
Radiographers can assess how the technology fits into scanning workflows.
Hospital administrators can examine scheduling and operational effects.
Technology specialists can evaluate integration with hospital information systems and imaging archives.
Regulators can establish whether the system meets applicable safety and performance requirements.
The stakeholder presentation therefore represents the beginning of a wider evaluation process rather than its conclusion.
DATA AND AI
Medical AI also raises questions about data.
AI systems are generally developed and evaluated using datasets.
The quality, diversity and representativeness of those datasets can affect system performance.
If a model is trained primarily on one population or one set of imaging conditions, its performance may vary when used elsewhere.
For Nigeria, this creates both a challenge and an opportunity.
Local data can help researchers develop systems that perform well in Nigerian healthcare environments.
At the same time, the use of medical data requires appropriate privacy, security and ethical safeguards.
Patient information cannot simply be treated as ordinary commercial data.
Any deployment of GenScan AI in clinical environments will therefore need to address data governance as well as technical performance.
PRIVACY AND SECURITY
Medical images contain sensitive patient information.
Hospitals need systems that protect that information from unauthorised access.
If AI software is connected to hospital networks, cybersecurity becomes part of the technology's safety profile.
The security of the application, the transmission of images and any cloud or local processing environment all matter.
Hospitals will also need to understand where patient data is stored, who can access it and how long it is retained.
Those considerations become particularly important if the technology is eventually deployed across multiple healthcare facilities.
AI DOES NOT REMOVE CLINICAL RESPONSIBILITY
Another important principle is that an AI tool should support clinical decision-making rather than replace professional responsibility.
GenScan AI is designed to reconstruct MRI images.
It is not itself the clinician responsible for diagnosing a patient.
A radiologist or other appropriately qualified healthcare professional remains responsible for interpreting medical images within the applicable clinical framework.
This distinction is essential to responsible AI adoption.
The faster production of an image does not eliminate the need for human expertise.
Instead, the technology's potential value lies in providing useful information more efficiently.
THE WAITING-LIST EFFECT
If the technology achieves its intended performance, one possible consequence is a reduction in MRI waiting times.
Hospitals could potentially schedule more examinations during the same operating period.
Patients could spend less time inside the scanner.
Facilities could potentially reduce backlogs.
Clinicians could receive diagnostic information sooner.
These benefits would not occur automatically.
They would depend on the entire hospital workflow.
If the scanner becomes faster but appointment scheduling remains inefficient, the overall waiting time may not fall by the same proportion.
If radiologists cannot interpret the additional images quickly enough, another bottleneck could emerge.
If maintenance or electricity interruptions remain frequent, increased theoretical scanner capacity may not translate into equivalent practical capacity.
The technology therefore has to be considered as part of a complete healthcare system.
MORE PATIENTS FROM EXISTING MACHINES
The strongest operational argument for GenScan AI is its potential to increase the utilisation of equipment already installed.
Suppose an MRI machine is available for a fixed number of hours each day.
If each compatible examination takes less time, more examinations could potentially be scheduled during those hours.
That could increase the number of patients served without immediately expanding the physical scanner fleet.
However, the exact increase would depend on clinical protocols, patient preparation, machine turnaround time and other factors.
The developer's claim that hospitals could potentially scan substantially more patients therefore represents a capacity proposition rather than a guarantee of a particular daily patient volume.
REDUCING THE BURDEN ON PATIENTS
MRI examinations can be uncomfortable for some patients because they require the person to remain still inside the scanner for an extended period.
Longer scans can be particularly challenging for children, elderly patients or people who find enclosed spaces difficult.
The developer says faster scans could also reduce repeat examinations and the need for sedation in some circumstances, although such clinical benefits would require appropriate validation.
A shorter examination could potentially improve patient experience.
But patient comfort cannot be considered separately from diagnostic accuracy.
The priority remains producing clinically useful images safely.
THE ROLE OF NIGERIAN UNIVERSITIES AND RESEARCHERS
The development of GenScan AI also highlights the potential role of Nigerian and Nigerian-linked researchers in advanced technology.
Artificial intelligence requires mathematics, computer science, engineering, domain expertise and access to computing resources.
Medical AI adds another layer because the technology has to interact with clinical knowledge.
Building such systems requires collaboration across disciplines.
Universities can provide research capacity.
Hospitals can provide clinical expertise and validation environments.
Technology companies can provide software engineering and commercialisation.
Government agencies can provide policy and regulatory frameworks.
Private-sector organisations can provide capital and market access.
The development of GenScan AI provides an example of how those elements can converge around a specific problem.
THE COMMERCIALISATION CHALLENGE
Winning a science prize does not automatically produce a commercial product.
The next stage can be more difficult.
A promising prototype must be tested, refined, validated, integrated and potentially certified.
Hospitals have procurement processes.
Doctors need confidence in the system.
Patients need assurance that their information is protected.
Regulators need evidence.
Investors need a viable business model.
The technology must also be maintained as MRI machines, software environments and clinical standards change.
For GenScan AI, the challenge now is translating recognition into sustained deployment.
FROM PROTOTYPE TO PRODUCT
The company behind GenScan AI says it is developing GenMRI as a software solution for faster medical imaging.
Its website describes the technology as capable of reducing scan times by up to 90 per cent on existing scanners and says the product is being developed to increase scanning capacity.
Those claims represent the company's product proposition.
Independent clinical evaluation remains important before broad adoption.
This distinction is particularly important in healthcare technology because performance claims have consequences for patient safety.
The process of validating an AI medical system should therefore be viewed as a strength rather than an obstacle.
It gives clinicians and patients evidence on which to base decisions.
WHY NIGERIA NEEDS THIS TYPE OF INNOVATION
Nigeria faces a combination of high healthcare demand and constrained resources.
The country needs more equipment, trained professionals, stronger health facilities and better access to diagnostic services.
At the same time, purchasing every additional piece of equipment required to meet rising demand can be expensive.
Technologies that increase the productivity of existing infrastructure could therefore complement physical investment.
That is one reason GenScan AI has attracted interest.
Its proposition is not that Nigeria can stop investing in MRI machines.
It is that software could potentially help existing machines do more while additional infrastructure is developed.
THE BROADER AI ECONOMY
The GenScan development comes as Nigeria expands its interest in artificial intelligence across different sectors.
The country has been building programmes around AI research, digital skills, public-sector technology and innovation.
The Federal Government has also been promoting initiatives designed to strengthen the country's technology ecosystem.
GenScan represents a more specialised form of that ambition.
Instead of a general-purpose AI tool, it applies artificial intelligence to a specific technical and healthcare problem.
Such specialised systems may become increasingly important as AI moves from experimentation into practical applications.
SPECIALISED AI VERSUS GENERAL AI
Much of the public conversation about AI focuses on general-purpose systems capable of generating text, images, software and other content.
Medical-imaging AI operates differently.
Its value depends on narrow technical performance.
A medical system must be accurate within defined clinical contexts.
Its outputs need to be evaluated.
Its limitations must be understood.
It also needs safeguards against inappropriate use.
GenScan AI therefore belongs to a growing category of specialised AI systems that seek to improve specific professional workflows.
Its potential significance will depend less on public excitement about artificial intelligence and more on measurable clinical performance.
THE NEXT TEST: REAL-WORLD EVIDENCE
The most important question now is how GenScan AI performs in real-world settings.
Laboratory or research results can establish technical potential.
Clinical use introduces additional variables.
Patients differ.
Machines differ.
Hospitals differ.
Protocols differ.
Radiologists have different workflows.
The technology will need to demonstrate that it can operate reliably within those conditions.
That is why Professor Nnaji's emphasis on engagement with clinicians is important.
Stakeholder evaluation can help identify where the technology performs well, where adjustments are needed and what safeguards should accompany deployment.
THE POSSIBILITY OF AFRICAN APPLICATIONS
If the technology proves effective, its relevance may extend across Africa.
Many African healthcare systems face similar challenges involving access to advanced diagnostic equipment, high patient demand and limited resources.
A software solution capable of increasing the productivity of existing MRI infrastructure could therefore have applications beyond Nigeria.
But each country's healthcare system would still require local evaluation.
Infrastructure, regulations, clinical protocols and machine fleets vary.
The technology's portability would therefore have to be demonstrated rather than assumed.
A SYMBOL OF LOCAL TECHNOLOGICAL CAPACITY
There is also a symbolic dimension to the innovation.
For years, discussions about Nigeria's technology sector have focused on fintech and consumer internet companies.
GenScan AI represents a different category.
It combines artificial intelligence, medical imaging and scientific research.
Its development suggests that Nigerian-linked innovators can participate in technically demanding areas that require advanced expertise.
That does not mean the country has solved its science and technology challenges.
But it demonstrates the type of innovation that policymakers and investors often seek when discussing a transition from technology consumption to technology creation.
WOMEN IN NIGERIAN TECHNOLOGY
Akoda's emergence as the winner also draws attention to women's participation in advanced technology and science.
She has been described as the first female winner of the Nigeria Prize for Science and Innovation and the youngest winner in the prize's history.
Her recognition provides visibility for women working in artificial intelligence, computer science and engineering.
Nigeria's technology ecosystem will require talent from across society if it is to expand.
Encouraging more young people, including women and girls, to pursue advanced scientific and technical fields can widen the country's pool of researchers and engineers.
THE $100,000 PRIZE AND WHAT IT REPRESENTS
The US$100,000 award attached to the NPSI is significant because advanced research often requires resources long after an idea has been demonstrated.
Funding can support further development, testing, staff, computing resources and commercialisation.
But prize money alone is unlikely to cover every stage required to bring a medical AI system to widespread clinical use.
Partnerships with hospitals, researchers, regulators, investors and technology companies can therefore become important.
The public presentation in Abuja provides an opportunity for those relationships to develop.
WHAT HAPPENS NEXT
The immediate next step is further stakeholder engagement.
Clinicians and technical experts will need to examine the system's performance and determine the appropriate pathway towards clinical application.
That includes assessing compatibility with existing MRI equipment.
It also includes establishing appropriate testing and validation protocols.
Regulatory requirements will need to be identified and satisfied.
Hospitals interested in adoption will need to assess infrastructure, workflow and cybersecurity considerations.
Only after those steps can the technology move from promising innovation to broader practical deployment.
THE REGULATORY QUESTION
Medical AI cannot be treated in the same way as an ordinary mobile application.
The consequences of errors can be serious.
A regulator may need evidence concerning performance, reliability, safety and intended use.
The exact regulatory pathway will depend on how GenScan AI is classified and how it is ultimately deployed.
If it is used to reconstruct images that clinicians interpret, the system's intended role and performance claims will be central to that assessment.
Clear documentation of limitations will also matter.
MAINTAINING HUMAN OVERSIGHT
Even if GenScan AI achieves the promised speed improvements, human oversight will remain essential.
The system produces reconstructed images.
Qualified healthcare professionals determine their clinical significance.
That means AI should function within an accountable medical workflow.
Hospitals should be able to identify when an AI-generated result was involved in the imaging process and maintain appropriate records.
Clinicians should also have procedures for dealing with images or cases in which the technology performs unexpectedly.
These safeguards are part of responsible technology adoption.
THE ECONOMIC POTENTIAL
Beyond healthcare, the innovation could have implications for Nigeria's technology economy.
A successful Nigerian medical-AI product could generate intellectual property, highly skilled jobs and export opportunities.
It could also encourage additional investment in specialised AI research.
That is particularly relevant as countries compete to build AI ecosystems that produce products rather than simply consume foreign services.
The commercial success of GenScan AI would ultimately depend on its clinical performance and business model.
But its development already demonstrates a pathway for technology companies to address high-value problems in specialised sectors.
A TEST FOR NIGERIA'S INNOVATION SYSTEM
GenScan AI is therefore also a test of the ecosystem around Nigerian innovation.
The country can identify and reward a promising technology.
The next challenge is helping that technology cross the gap between award-winning research and a product that can operate safely and sustainably in hospitals.
That requires funding.
It requires technical validation.
It requires clinical partnerships.
It requires regulation.
It requires procurement pathways.
And it requires customers willing to adopt the technology when evidence supports its use.
If those pieces come together, the innovation could become more than a prize-winning project.
CONCLUSION
Nigeria's technology sector has gained a fresh example of locally developed artificial intelligence being applied to a highly specific and practical problem.
GenScan AI, developed by Mary-Brenda Akoda, won the 2026 Nigeria Prize for Science and Innovation after competing against 236 other entries from a record 237 submissions. The technology uses the C-MORE algorithm to reconstruct MRI images through a one-step framework and is designed to potentially reduce MRI scan acquisition times by up to 90 per cent without requiring new MRI hardware.
The potential implications are significant.
If the technology performs as intended in appropriate clinical settings, hospitals could potentially use existing MRI machines more efficiently, reduce waiting periods and increase the number of patients served within available operating time.
But the technology's promise should not be confused with universal clinical readiness.
The 90 per cent figure is a potential maximum rather than a guarantee for every examination, and the system will need appropriate validation across machines, protocols and clinical settings.
The presentation of GenScan AI to health and technology stakeholders in Abuja is therefore an important transition point.
The innovation has moved from competition recognition towards discussions about practical application.
The next stage will involve clinicians, technical experts, healthcare institutions and relevant authorities examining whether the technology can deliver its proposed benefits safely and consistently.
For Nigeria, the development is significant for another reason.
It shows how artificial intelligence can be directed toward problems that are directly connected to the country's infrastructure constraints.
Rather than treating AI only as a general-purpose productivity tool, researchers are applying it to medical imaging, where improving the efficiency of existing equipment could have tangible consequences for healthcare delivery.
The development also highlights the role of Nigerian scientific talent in creating technology with potential international relevance.
Akoda's background in computer science, artificial intelligence research and industry demonstrates the increasingly global pathway through which Nigerian innovators can acquire expertise and apply it to local problems.
The real measure of GenScan AI, however, will come after the award.
It will be measured by clinical evidence, regulatory compliance, compatibility with hospital systems, patient safety and whether healthcare providers can translate faster scanning into better access to diagnosis.
If those requirements are successfully met, GenScan AI could become an example of how software can complement physical healthcare infrastructure.
If further testing identifies limitations, those findings will also be valuable because responsible innovation depends on knowing precisely where a technology works and where it does not.
Either way, the development represents an important moment for Nigeria's technology ecosystem.
The country is not simply discussing artificial intelligence as a future possibility.
Researchers and technology developers are increasingly attempting to apply it to concrete problems in medicine, education, agriculture, finance and other sectors.
GenScan AI places medical imaging within that expanding Nigerian technology story.
Its next chapter will be determined not by the size of the award or the excitement surrounding the 90 per cent figure, but by the evidence produced when the technology is tested, evaluated and, where appropriate, introduced into real healthcare environments.
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