‘Challenges and opportunities for imaging on a global scale’ special collection—introductory editorial
Erasmo de la Peña, Michael Jackson, Sonal Krishan, Ntobeko Ntusi, Sarah Sheard

Abstract
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TopicsRadiomics and Machine Learning in Medical Imaging
Globally, 4.5 billion people lack access to diagnostic imaging.1 It is not uncommon that in low- and middle-income countries and, in fact, pockets in high-income countries, many rural clinics are equipped with machines but lack trained technologists as well as radiologists, cardiologists or other specialists to operate them. This is not merely a logistical shortcoming–it is a moral failure. The potential of medical imaging to alleviate suffering—through early detection, accurate diagnosis, and timely intervention–is useless if only confined to urban hubs or high-income countries. Global imaging inequality is further exacerbated by a tendency to over image in well-resourced centers, often for conditions that would be better addressed by preventative measures than costly imaging. Most imaging specialists working in a high-income setting would also acknowledge a significant proportion of their imaging workload is of doubtful clinical benefit to the patient, but performed for fear of litigation, questionable protocols, or as a means to expedite discharge from the emergency department.2–4 Rebalancing radiology resources may confront some of us with the need to consider doing less imaging, not more.5–8
Encouragingly, some solutions are already within reach:
Yet equity alone is not enough. As we push forward, we must address the environmental impact of medical imaging. Healthcare is responsible for 4.4% of global greenhouse gas emissions, with imaging a significant contributor. A single MRI can consume as much electricity as an average household uses in three months. In drought-prone regions, the water and energy costs of imaging become even more critical. Justification of imaging can no longer be confined to the narrow risk: benefit calculation of the patient being examined.9^,^10
We must advocate for:
Sustainable radiology is not optional—it is our duty to future generations.
We are now at a pivotal moment. The convergence of AI, accessibility, and sustainability demands a new breed of imaging specialist. This profession blends technical mastery with ethical stewardship, leveraging tools not for prestige but for planetary and patient well-being. An imaging specialist’s choices–whether to embrace ethical AI, support rural colleagues, or reduce environmental impact–can carry global implications.
As co-Guest Editors of this special collection in BJR|Open, we invite our colleagues around the world to reimagine the purpose of medical imaging. Let us move from the ‘I’ of individual achievement to the ‘we’ of shared responsibility. We hope to see high-quality original research and review manuscripts submitted in the following areas:
We hope this carefully curated collection will grow and develop over time, generate interest in the community, and lead to a paradigm shift. We look forward to seeing your submissions and hope you enjoy reading the articles.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1World Health Organization, International Bank for Reconstruction and Development/The World Bank. Data, Analytics & Delivery for impact (DDI), Health Systems Governance and Financing (HGF). p. 160.
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- 4Plasencia-Martínez JM , Sánchez-Canales M, Otón-González E, et al Inappropriate requests for cranial CT scans in emergency departments increase overuse and reduce test performance. Emerg Radiol. 2023;30:733-741. 10.1007/s 10140-023-02185-y. Epub 2023 Nov 16. Erratum in: Emerg Radiol. 2024 Feb; 31(1):123. 10.1007/s 10140-023-02194-x. PMID: 37973624.37973624 · doi ↗ · pubmed ↗
- 5Frija G , BlažićI, Frush DP, et al How to improve access to medical imaging in low-and middle-income countries? E Clinical Medicine. 2021;38:101034. 10.1016/j.eclinm.2021.10103434337368 PMC 8318869 · doi ↗ · pubmed ↗
- 6Everton KL , Mazal J, Mollura DJ, RAD-AID Conference Writing Group White paper report of the 2011 RAD-AID Conference on International Radiology for Developing Countries: integrating multidisciplinary strategies for imaging services in the developing world. J Am Coll Radiol. 2012;9:488-494. 10.1016/j.jacr.2012.01.00522748790 PMC 4844552 · doi ↗ · pubmed ↗
- 7Schlemmer HP , Bittencourt LK, D’Anastasi M, et al Global challenges for cancer imaging. J Glob Oncol. 2018;4:1-10.10.1200/JGO.17.00036 PMC 618075930241164 · doi ↗ · pubmed ↗
- 8RAD-AID International. RAD-AID Radiology Readiness Survey; 2013. [cited 2020 Nov 30]. Accessed July 7, 2025. https://www.rad-aid.org/resource-center/radiology-readiness
