A Systematic Review of Diagnostic Radiology Access, Barriers, and Novel Interventions in Low-Income Countries
Bibliographic record
Abstract
Introduction: Diagnostic radiology is an essential component of modern healthcare, yet billions of people in Low-Income Countries (LICs) lack access. This disparity, which impacts outcomes for both communicable and non-communicable diseases, remains a neglected area in global health policy. This review systematically synthesizes the evidence on radiology access, barriers, and the impact of novel interventions in LICs. Methods: This systematic review was conducted following PRISMA guidelines. We searched PubMed, Google Scholar, Semantic Scholar, Springer, Wiley Online Library for studies published between 1 January 2018 and 31 December 2025. We included primary studies and surveys focused on LICs (per World Bank GNI ≤ 1,135) that reported on outcomes related to radiology access, barriers, or interventions. Quality assessment was performed using the ROBINS-I and Newcastle-Ottawa Scale (NOS) tools. Results: Sixteen studies met the inclusion criteria. The results demonstrate a significant and catastrophic deficit in conventional imaging and workforce, with less than one CT scanner per million inhabitants in LICs and diagnostic availability near 0% at the primary care level. This gap is linked to severe outcome disparities, including a 3-month stroke mortality rate 4.5 times higher in LMICs than in HICs (7.7% vs. 1.7%). However, the review also identified significant evidence for novel interventions. Teleradiology implementation in the Democratic Republic of Congo changed patient diagnosis in 62% of cases and management in 41%. AI-assisted diagnostics show significant cost-effectiveness for conditions like tuberculosis in Malawi. Point-of-Care Ultrasound (POCUS) emerges as a critical, high-impact tool, though its implementation remains profoundly limited. Discussion: The evidence confirms a "diagnostic void" in LICs, driven by an ecosystem of barriers including lack of maintenance, cost, and workforce deficits. The significance of these findings is twofold: the access gap is directly linked to preventable mortality, and technological interventions provide a proven, cost-effective, and scalable "leapfrog" pathway to bridging this gap. Conclusion: LICs must prioritize a dual strategy: shoring up basic infrastructure for X-ray and ultrasound while simultaneously scaling up high-impact, technologically-driven solutions like POCUS, AI-assisted diagnosis, and teleradiology. Future investment must shift from sporadic equipment donation to building sustainable human and technical infrastructure.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".