Portable neuroimaging and AI: democratizing brain diagnostics introduction

Abstract

Advanced neuroimaging, particularly magnetic resonance imaging (MRI), is central to modern neurological diagnosis; yet access to MRI remains profoundly unequal (1)(2)(3)(4). Conventional scanners are expensive, infrastructure intensive, and concentrated in large urban hospitals, leaving rural and low-income regions underserved; approximately 66% of the global population does not have access to an MRI scanner (3,4). Furthermore, low-and-middle income countries (LMICs) average approximately 1.1 MRI units per million inhabitants compared with more than 25 per million in high income countries, meaning that a majority of the global population lacks timely access to brain imaging (5). In Latin America and the Caribbean, the average is roughly 3 to 4 scanners per million people, reflecting a persistent structural inequity in neurological care (6); as a result, strokes, brain tumors, trauma, infections, and pediatric neurological disorders are frequently diagnosed late or not at all (7). Recent technological advances offer a credible path to narrowing this gap. Ultra-portable, low-cost neuroimaging devices, combined with artificial intelligence (AI) driven image reconstruction and diagnostic support, can bring brain imaging directly to the point of care, at a lower cost (5,8). Therefore, in this opinion article I argue that the convergence of portable neuroimaging and AI represents a public health opportunity to improve neurological disease prevention at primary, secondary, and tertiary levels, particularly in underserved regions of Latin America where economical and accessibility constraints have long affected public health. Beyond technical innovation, these tools enable a rethinking of how neurological diagnostics are delivered, shifting from centralized, specialist dependent systems to more distributed and equitable models of care that fit the countries' realities rather than setting an unrealistic standard that could only be met in resource-rich settings. Therefore, this opinion goes beyond a technical review by proposing a structured, equity-oriented framework for integrating portable neuroimaging and artificial intelligence into health systems; specifically, I argue for a tiered, decentralized model in which low-field MRI and AI-supported diagnostics are included in primary and secondary care levels for triage, early detection, monitoring, and clinical decision making while complex cases are escalated to tertiary centers with specialist oversight. This approach reframes portable neuroimaging not as a substitute for high-field systems, but as a complementary public health tool designed to reduce structural inequities in access to neurological diagnostics and care in areas with limited resources such as LMICs.Limited access to imaging is a major determinant of poor neurological outcomes in LMICs (9,10). Traditional MRI systems cost well over one million US dollars and require shielded rooms, cryogenic cooling, stable power supply, and highly trained personnel; even when equipment exists, workforce shortages remain an important issue (9,10). Many countries in Latin America and Africa have fewer than three radiologists per 100,000 inhabitants outside major cities, leading to delays in image interpretation and treatment initiation (11,12). In this context, the challenge is not merely technological but systemic.Portable MRI addresses these constraints by radically reducing size, power requirements, and operational complexity. Ultra-low field systems, typically operating below 0.1 Tesla, can be wheeled to the bedside, powered by a standard electrical outlet, and operated by non-specialist staff after brief training (13)(14)(15). The most widely deployed example to date is the Hyperfine Swoop system, a 0.064 Tesla scanner costing a fraction of conventional MRI and requiring no dedicated suite or cryogens (13,16). By lowering infrastructure and staffing thresholds, such devices make advanced imaging feasible in district hospitals, community clinics, and mobile health units. Furthermore, studies have proven their validity in relevant neurological conditions (13)(14)(15); for instance, a 2021 study looking into intracerebral hemorrhage, using a 0.064T portable MRI unit, reported an 80.4% sensitivity (95%CI, 68-90) and 96.6% specificity (95%CI, 90-99) with an excellent intraclass correlation coefficient (0.955) for hematoma volumes when compared to standard imaging (14). Crucially, portability shifts the care paradigm; rather than transporting unstable patients over long distances, imaging can occur where the patient is, including intensive care units and emergency settings. This is particularly relevant in regions with geographic barriers, limited ambulance networks, and high out of pocket travel costs, all of which disproportionately affect rural and indigenous populations in LMICs (17). To facilitate interpretation of the current evidence base, Table 1 The clinical promise of portable neuroimaging is already being explored across diverse low resource settings. Pilot deployments have occurred in parts of Africa, South Asia, and Latin America, targeting conditions such as hydrocephalus, stroke, traumatic brain injury, and pediatric infections (18)(19)(20)(21). In Uganda, for example, low field MRI has been used to evaluate children with hydrocephalus, offering radiation free imaging where CT had previously been the only option (22). Similar initiatives have demonstrated feasibility in settings where conventional MRI has never been available. However, these benefits must be balanced against technical limitations. Ultra-low field MRI produces lower spatial resolution and signal to noise ratio than standard 1.5 or 3 Tesla scanners; scan times are longer, and advanced sequences such as high-resolution angiography or contrast enhanced studies are currently limited (15,23,24). Subtle lesions may be missed, and image interpretation requires adjusted expectations (15,23,24).However, in many underserved contexts, the relevant comparison is not between low-field and high-field MRI, but between low-field MRI and no imaging at all. For major pathologies such as intracranial hemorrhage, large ischemic strokes, hydrocephalus, mass lesions, and gross structural abnormalities, portable MRI has shown adequate diagnostic performance (13,14,18,19,25). Ongoing advances in AI based reconstruction and denoising are further improving image quality, narrowing the practical gap with conventional systems (26)(27)(28).Rather than replacing high-field MRI, portable neuroimaging is best understood as a component of a tiered diagnostic ecosystem. Within this framework, its primary role is to enable decentralized triage, early detection, longitudinal monitoring, and risk management at lower levels of care, while preserving referral pathways for advanced imaging and specialist evaluation. This model aligns with the operational realities of LMIC health systems and represents a shift from technology-centric adoption to system-level integration aimed at maximizing the impact at the population level.Artificial intelligence is integral to the viability and scalability of portable neuroimaging. At the acquisition level, AI driven reconstruction algorithms enhance image quality in real time, compensating for the physical constraints of low magnetic field strength (26)(27)(28). Deep learning based denoising, artifact correction, and super resolution techniques have transformed previously marginal images into clinically interpretable scans (26)(27)(28). Beyond reconstruction, AI offers the potential to democratize image interpretation. Automated algorithms can detect and highlight abnormalities, prioritize urgent findings, and provide preliminary diagnostic suggestions (29). Multiple studies have shown that AI systems can identify strokes, hemorrhages, and space occupying lesions with accuracy comparable to expert radiologists under defined conditions (30)(31)(32)(33)(34). The World Health Organization has recognized AI as a potential tool to mitigate global shortages of diagnostic specialists, particularly in imaging intensive fields or in settings where specialists are scarce (35). I want to be clear, I'm not advocating for a replacement of radiologists or neuroradiologist by AI and low-field systems handled by less personnel; rather, the use of AI and low-field systems should be reserved for primary or even secondary centers, with limited resolution capacity, economic resources and accessibility constraints with clear guidelines to further direct selected patients to tertiary care where they can receive proper high-field imaging (when required) interpreted by a specialist.In this manner, we can save both resources and decrease public expenditure while also incrementing the access to proper diagnosis and care to areas where this is non-existent and considered a luxury. In practical terms, AI enables a model in which a portable scanner operated by general health workers can be paired with automated decision support and remote specialist oversight. Scans acquired in rural clinics can be uploaded to cloud-based platforms, where AI performs initial analysis and flags critical cases for tele-radiology review or referrals to tertiary centers with resolution capabilities; this hybrid model preserves clinical accountability while dramatically extending specialist reach. In Figure 1, I propose a potential conceptual framework of how I envision these hybrid application of AI assisted tele-radiology. Nevertheless, responsible deployment is essential. Algorithms and guidelines must be validated on local populations and low-field data to avoid performance degradation. Clinicians must be trained to understand AI outputs, including their limitations, and governance frameworks must address data privacy, bias, and accountability. When implemented thoughtfully, AI does not replace clinicians but amplifies their capacity, making advanced diagnostics feasible where they were previously unattainable; however, while AI has demonstrated performance comparable to expert radiologists under controlled conditions, its deployment in real-world, low-resource settings requires careful validation, as differences in imaging quality, population characteristics, and disease patterns may affect performance and reliability (30)(31)(32)(33)(34). A summary of major AI applications in neuroimaging and their principal limitations is provided in Table 2. Within this proposed decentralized neuroimaging framework, the public health relevance of portable MRI and AI extends across all levels of prevention, linking diagnostic accessibility with population-level disease control strategies. At the level of primary prevention, expanded access to imaging can reveal silent or early pathology that informs risk reduction strategies. Detection of asymptomatic lesions, early vascular changes, or regional disease patterns can guide targeted interventions and public health policies (36,37). In endemic areas, imaging data may highlight preventable causes of neurological disease such as parasitic infections or poorly controlled hypertension, strengthening the case for upstream interventions (36,38,39).Secondary prevention represents the most immediate impact. Early diagnosis is critical in conditions such as stroke, traumatic brain injury, central nervous system infections, and epilepsy; delays in imaging are a major contributor to preventable disability and death in LMICs (40)(41)(42). Portable MRI deployed in regional centers or emergency settings could shorten diagnostic timelines, enabling timely thrombolysis, surgical referral, or antimicrobial treatment. In epilepsy, access to imaging can identify surgically treatable lesions that would otherwise remain undetected, transforming lifelong disease trajectories (42). Tertiary prevention is equally important; chronic neurological conditions require ongoing monitoring to prevent complications and deterioration. Portable imaging facilitates follow-up for stroke survivors, post-surgical patients, and children with congenital conditions, reducing dependence on distant tertiary hospitals. Earlier detection of complications such as shunt malfunction, recurrent tumors, or silent infarcts allows intervention before irreversible harm occurs (43)(44)(45).By embedding portable and easily accessible imaging into longitudinal care, health systems could potentially reduce disability, improve quality of life, and lower long-term costs.The incorporation of portable neuroimaging and AI into healthcare systems requires an understanding within a life-course and equity framework, wherein access to diagnostic tools affects disease trajectories from early childhood to advanced age. In pediatric populations, conditions like hydrocephalus require repeated imaging for diagnosis and follow-up; however, access to MRI is often restricted in many low-resource settings, where CT frequently represents the sole available modality, if any (19,22). Portable MRI provides a radiation-free and more accessible option, facilitating safer longitudinal monitoring and earlier intervention. In epilepsy, which affects more people in low-and middle-income countries than in high-income countries, limited access to neuroimaging leads to missed diagnoses and delays in finding surgically treatable lesions, that if operated upon, would result in a reduction of years lived with disability (42).In adulthood, prompt diagnosis of acute neurological conditions, including stroke and traumatic brain injury, is essential to prevent disability and mortality; however, delays in imaging is still a significant obstacle in resource-limited environments (40,41). Portable MRI used at secondary or regional centers could speed up the process of making a diagnosis and help doctors decide on treatment sooner. In later life, imaging is important for finding silent cerebrovascular disease and keeping an eye on neurodegenerative processes that lead to cognitive decline (43). Detecting these conditions sooner may allow for preventive measures and lessen the long-term strain on healthcare systems. Certainly, structural determinants of health have a big impact on these life-course trajectories. The availability of neuroimaging in LMICs is heavily affected by how the health system is funded, how infrastructure is spread out, and how many workers are available; most services are located in urban tertiary centers (6,9,10). Because of this, people living in rural areas, indigenous communities, and other marginalized groups have to deal with significant issues like long travel times, high out-of-pocket costs, and long wait times for referrals (17). Portable MRI breaks down these barriers by spreading out imaging capacity, which makes it possible to get diagnostic services closer to where care is needed. When used with AI-assisted interpretation and telemedicine networks, this method can help with the lack of specialists and make neurological diagnostics more accessible across regions (35).In my opinion, portable neuroimaging and AI are not merely technological advancements; they are tools capable of transforming neurological care pathways throughout the lifespan, especially in contexts where systemic inequities have traditionally hindered access to prompt diagnosis and treatment.Technology alone is insufficient to achieve equitable impact. Successful integration of portable neuroimaging and AI requires supportive policy frameworks, sustainable financing, and workforce development; governments and donors should explore acquisition models that lower entry barriers for public hospitals, including pooled purchasing, leasing, or pay per scan arrangements. Training programs must equip general clinicians and nurses with the skills to operate devices and interpret results within defined protocols that prioritize patients' safety.Certainly, interoperability and referral pathways are critical. Portable imaging must be linked to telemedicine networks and referral centers to ensure continuity of care (Figure 1). Maintenance, quality assurance, and data governance should be planned from the outset, although the simplified design of low-field systems reduces many traditional barriers.Skepticism regarding image quality and implementation feasibility is therefore justified. While early experience suggests that clinically meaningful benefits can be achieved with images that are 'good enough' rather than perfect (46), successful deployment depends on careful integration into health systems, including training, maintenance, regulatory alignment, and clear referral pathways. In underserved settings, the ethical considerations often favor wider access over maximal technical sophistication; however, this must be balanced against the risks of inappropriate use, over-reliance on automated systems, and unequal distribution of benefits.Regardless of their promise, the implementation of portable neuroimaging and AI is associated with several important limitations that must be critically considered. First, algorithmic bias remains a significant concern, as many AI models are trained on datasets originated from highincome settings and high-field imaging, which may limit their generalizability to low-field data and diverse populations (26)(27)(28)35). Without local validation, there is a risk of reduced diagnostic accuracy or systematic misclassification in underrepresented groups. Second, sustainability and maintenance pose practical challenges; although portable MRI reduces infrastructure requirements, devices still require specialized technical support, quality assurance, and reliable supply chains, which may be difficult to ensure in resource-limited settings (9,10). Third, cost-effectiveness must be interpreted cautiously. While acquisition costs are lower than conventional MRI, long-term expenses related to maintenance, training, data storage, and connectivity may affect initial savings, particularly in fragmented health systems (5,6). Fourth, regulatory and reimbursement frameworks in many LMICs are not yet adapted to support AI-assisted diagnostics or decentralized imaging models, potentially limiting scalability and integration into national health systems (35). Finally, there is a risk of overreliance on AI in settings with limited specialist oversight; while AI can support decisionmaking, unregulated dependence without adequate training or referral pathways may lead to diagnostic errors or delayed referral of complex cases. For example, implementation experiences in resource-constrained settings such as Malawi have demonstrated that, although portable MRI can be successfully integrated into clinical workflows, challenges related to infrastructure, training, and maintenance remain important considerations for scalability (20). Therefore, proper training, implementation protocols, oversight and regulatory control are necessary to properly implement the suggested model.This opinion proposes a health system-oriented framework in which portable neuroimaging and artificial intelligence are integrated into a tiered, decentralized model of neurological care; by aligning technological innovation with levels of prevention and referral pathways, this approach shifts the focus from isolated diagnostic capability to equitable system design. In doing so, it provides a conceptual basis for policymakers and clinicians to implement scalable solutions that address longstanding disparities in access to brain imaging and neurological care in LMICs. The potential impact of this model spans prevention, from early risk identification to timely diagnosis and long-term disease management. In Latin America and similar regions, where neurological disease burden is high and specialist resources are unevenly distributed, portable MRI and AI supported diagnostics can strengthen health system resilience and responsiveness; they transform advanced neuroimaging from a centralized luxury into a scalable public good. However, realizing this potential will require coordinated action from clinicians, researchers, policymakers, and industry. Technical limitations, training needs, and governance challenges must be addressed transparently; yet the direction of travel is clear.Portable neuroimaging and AI have given global health the tools to extend the frontiers of neurological care and the task now is to deploy them wisely, inclusively, and with a focus on population level benefit, ensuring that advances in brain diagnostics serve not only the few, but the many, moving away from hard-to fulfil diagnostic requirements that are often only possible in resource-rich settings with access to cutting edge technology.

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