Looking towards the future of MRI in Africa
Bibliographic record
Abstract
Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool within modern healthcare, yet its availability remains largely confined to high-income nations. The imperative to extend MRI accessibility to lower-income countries aligns with the pursuit of universal health coverage, a key target of the UN’s Sustainable Development Goal 3. In an interview with Nature Communications, three scientists dedicated to advancing MRI accessibility in Africa share their insights. These experts include Dr Udunna Anazodo (Assistant Professor at McGill University, Canada and Scientific Director, Medical Artificial Intelligence (MAI) Lab, Lagos, Nigeria), Dr Johnes Obungoloch (Lecturer at Mbarara University of Science and Technology, Uganda) and Dr Ugumba Kwikima (Neuroradiologist, Lugalo General Military Hospital, Tanzania). Our discussion considers the current MRI landscape across African countries and the associated challenges and opportunities. We also cover technological innovations making a difference, such as low field MRI, alongside the role of advocacy initiatives in bolstering accessibility. We finally look ahead to the future of MRI in Africa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".