‘Challenges and opportunities for imaging on a global scale’ special collection—introductory editorial
Challenges and opportunities for imaging on a global scale' special collection-introductory editorialGlobally, 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 highincome 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]3][4] Rebalancing radiology resources may confront some of us with the need to consider doing less imaging, not more. [5]][7][8] Encouragingly, some solutions are already within reach:
Authors
- Erasmo de la Peña-Almaguer (ORCID: https://orcid.org/0000-0003-3123-9594)
- Sarah Sheard
- Ntobeko Ntusi (ORCID: https://orcid.org/0000-0003-2347-7883)
- Michael R. Jackson (ORCID: https://orcid.org/0000-0002-3377-0138)
- Sonal Krishan
Institutions
- South African Medical Research Council (ZA)
- Medanta The Medicity (IN)
- Imperial College Healthcare NHS Trust (GB)
- Royal Hospital for Children (GB)
- Royal Edinburgh Hospital (GB)
- Hospital Zambrano Hellion (MX)
- Medanta The Medicity (IN)
- Tecnológico de Monterrey (MX)
Publication Details
- Journal
- BJR|Open
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1093/bjro/tzaf023
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00