AI Breakthrough Could Cut MRI Scan Times by 90% as Nigerian Innovation Takes Centre Stage

ARTIFICIAL intelligence is opening new possibilities in healthcare, from helping clinicians interpret medical images to improving the efficiency of diagnostic equipment. In Nigeria, where healthcare facilities face growing demand for diagnostic services, one homegrown innovation is drawing attention to how advanced computing could help address longstanding challenges in medical imaging.

Mary-Brenda Akoda, winner of the 2026 The Nigeria Prize for Science and Innovation (NPSI), presented her award-winning technology, GenScanAI, at Vibranium Valley in Ikeja, Lagos, on Wednesday 14 October 2026. The presentation brought together medical practitioners, researchers, academics, students and technology stakeholders to examine the innovation and discuss its potential applications.

At the centre of the demonstration was a striking proposition: GenScanAI could reduce magnetic resonance imaging (MRI) scan times by as much as 90%. If clinical evaluation confirms that potential across relevant examinations and patient groups, the technology could help diagnostic centres serve more patients without immediately investing in additional MRI hardware.

However, the significance of the innovation extends beyond speed. Its developers are also seeking to improve image quality and address challenges associated with patient movement during scans. Together, these capabilities could offer healthcare providers another way to improve diagnostic efficiency while making better use of existing infrastructure.

The Problem Behind Long MRI Waiting Times

MRI plays an important role in modern medicine. Clinicians use it to examine soft tissues, organs, joints and other structures in the body, supporting the investigation and management of a wide range of medical conditions. Yet the examination can take considerable time, depending on the body part being scanned, the imaging sequences required and the patient’s circumstances.

Consequently, long examinations can constrain the number of patients a facility can accommodate. When demand exceeds available scanning capacity, patients may face delays in obtaining appointments or receiving diagnostic information. Such delays can complicate treatment planning, particularly when clinicians need timely imaging to guide decisions.

The pressure can be especially significant in health systems where access to diagnostic equipment is limited and investment in additional machines is expensive. Purchasing MRI equipment involves more than the initial price of the scanner. Facilities must also consider installation, maintenance, electricity, staffing, supporting infrastructure and the availability of trained personnel.

Against this background, technologies that improve the performance of existing equipment deserve attention. Rather than relying exclusively on expanding the number of scanners, healthcare providers could explore ways to increase the efficiency of the machines already available.

GenScanAI is positioned within that opportunity. By applying artificial intelligence to MRI image reconstruction, the innovation seeks to reduce the time required to produce useful medical images while maintaining or improving image quality.

How GenScanAI Applies Artificial Intelligence

MRI systems generate data that must be processed to produce images clinicians can examine. Image reconstruction is therefore a critical stage in the imaging process. Artificial intelligence can help analyse patterns in imaging data and support the reconstruction of images from information acquired during a scan.

GenScanAI applies AI and advanced computing techniques to this process. Its stated potential to reduce MRI acquisition time by up to 90% suggests that it aims to make the imaging workflow substantially more efficient.

Nevertheless, the distinction between scan acquisition time and the full time required for a patient’s examination is important. A reduction in the acquisition time for a particular imaging sequence does not automatically translate into an equivalent reduction in the patient’s entire visit. Preparation, positioning, safety screening, image review and other clinical procedures may still require time.

Similarly, faster image reconstruction must not come at the expense of diagnostic reliability. Medical images need to preserve the features clinicians depend on to identify abnormalities and make informed decisions. Any AI-assisted system must therefore demonstrate that its images are sufficiently accurate and reliable for the intended clinical uses.

These questions make technical testing and clinical validation essential. The reported 90% reduction is a potentially significant performance claim, but its practical meaning depends on the examinations tested, the comparison method used and the quality of the resulting images.

Better Use of Existing MRI Infrastructure

One of the most attractive aspects of GenScanAI is its potential to improve MRI services without requiring hospitals to replace their existing machines or immediately purchase new ones.

If the technology can safely shorten examinations, facilities could potentially schedule more patients within the same operating hours. That could improve equipment utilisation and help reduce backlogs, particularly in busy diagnostic centres.

For patients, shorter examinations may also make the experience more manageable. Remaining still inside an MRI scanner can be uncomfortable, especially for people who experience anxiety, pain or difficulty maintaining a fixed position. Reducing the time spent in the scanner could help ease some of these challenges, although the degree of benefit would depend on the patient’s condition and the type of examination.

Akoda’s innovation also raises the possibility of addressing patient motion, which can affect image quality. Movement during a scan may introduce distortions or blur, potentially making images more difficult to interpret. GenScanAI’s stated potential to accommodate patient motion could therefore be valuable if testing demonstrates consistent improvements under real clinical conditions.

Even so, faster scanning alone cannot resolve every obstacle facing medical imaging in Nigeria. Facilities still need reliable power, trained radiographers, radiologists, maintenance support and affordable access for patients. AI may help improve operational efficiency, but it cannot replace the wider investments required to strengthen diagnostic services.

NLNG Calls for Wider Engagement With the Innovation

Sophia Horsfall, NLNG’s General Manager, External Relations and Sustainable Development, described the innovation as a response to an important healthcare need.

She noted that medical imaging plays a central role in diagnosis and treatment, while the time required for MRI examinations can delay care and limit the number of patients facilities can serve.

According to Horsfall, GenScanAI illustrates how advances in artificial intelligence and computing can be applied to practical healthcare challenges and help improve the efficiency of existing medical infrastructure.

She also explained that the public presentation was designed to take the winning innovation beyond the award adjudication process. By bringing Akoda together with healthcare professionals, researchers and students, the event created an opportunity for potential users and collaborators to engage directly with the work.

That engagement matters because promising research often requires more than recognition to become a practical solution. Developers need opportunities to explain how their technology works, answer technical questions, identify suitable clinical applications and establish partnerships with institutions capable of testing and deploying it.

For GenScanAI, conversations with medical practitioners and researchers could help clarify where the technology offers the greatest value and what evidence would be required before wider adoption.

From Scientific Recognition to Clinical Evidence

Akoda emerged as the winner of the 2026 Nigeria Prize for Science and Innovation after an evaluation process involving more than 230 entries. The competition focused on the theme, “Innovations in Artificial Intelligence or ICT or Digital Technologies for Development.”

The theme reflects growing interest in the use of digital technologies to address challenges in healthcare, education, public services and other sectors. In this case, the recognition places a spotlight on an innovation seeking to connect AI research with a practical medical need.

However, winning a science prize and achieving widespread clinical adoption are distinct milestones. The next stage requires clear evidence of performance, safety, compatibility and value in real healthcare settings.

Hospitals and diagnostic centres would need to understand which MRI systems GenScanAI can support, how it integrates into existing workflows, what training users require and whether the technology can operate reliably under local conditions. Independent evaluation would also help establish whether its reported speed improvements are accompanied by acceptable diagnostic quality.

Cost is another consideration. Although the innovation is presented as not requiring new MRI hardware, healthcare providers would still need to assess software costs, computing requirements, implementation support, maintenance and any necessary upgrades.

Regulatory requirements and patient-data protection would also be central to deployment. Medical AI systems must handle sensitive information responsibly and provide appropriate safeguards against errors, unauthorised access and unreliable outputs.

A Potential Step Towards More Efficient Healthcare

The presentation of GenScanAI in Lagos has brought attention to the possibilities of Nigerian-led research in medical technology. Its promise lies not simply in using AI, but in applying it to a problem that directly affects the efficiency of diagnostic services.

If further testing supports its claimed performance, the innovation could help hospitals make better use of existing MRI equipment, shorten certain examinations and improve patient access to diagnostic imaging.

Yet the wider impact will depend on what follows the public demonstration. Clinical validation, transparent performance evidence, regulatory compliance and partnerships with healthcare institutions will determine whether the technology can move from an award-winning innovation to a dependable clinical tool.

For now, GenScanAI represents a promising direction for digital health research in Nigeria. Its ultimate contribution will be measured not by the headline figure alone, but by whether it can deliver faster, reliable and accessible medical imaging for patients and healthcare providers.