A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies
Question How can LMICs overcome infrastructure, data, workforce, regulatory, and financing barriers to implement artificial-intelligence tools in clinical medical imaging? Findings Our multinational consensus identifies five obstacles and maps each to actionable levers: low-cost hardware, regional data hubs, train-the-trainer schemes, harmonized regulation, blended financing. Clinical relevance Implementing these targeted measures enables LMIC health systems to deploy AI imaging reliably, shorten diagnostic delays, and improve patient outcomes while reducing dependence on external expertise.
Authors
- Sola Adeleke (ORCID: https://orcid.org/0000-0002-4388-8754)
- Rama Chellappa
- Muhammad Umair (ORCID: https://orcid.org/0000-0001-6113-8335)
- Ahmed Afifi (ORCID: https://orcid.org/0000-0002-7236-225X)
- Ona Ambrozaite (ORCID: https://orcid.org/0000-0003-1464-2111)
- Ritu Agarwal (ORCID: https://orcid.org/0000-0002-8907-5652)
- Ahmed Marey (ORCID: https://orcid.org/0000-0002-3659-1696)
Institutions
- Kennedy Krieger Institute (US)
- Johns Hopkins University (US)
- William Carey University (US)
- King's College London (GB)
- Benha University (EG)
- Johns Hopkins Medicine (US)
- Shaikh Khalifa Medical City (AE)
Publication Details
- Journal
- European Radiology
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1007/s00330-025-12031-z
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.27