Journal article
Generalizability assessment of AI models across hospitals in a low-middle and high income country
- Abstract:
- The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income countries (HICs), which often possess the resources and infrastructure to adopt innovative healthcare technologies, LMICs confront resource limitations such as insufficient funding, outdated infrastructure, limited digital data, and a shortage of technical expertise. Consequently, many algorithms initially trained on data from non-LMIC settings are now being employed in LMIC contexts. However, the effectiveness of these systems in LMICs can be compromised when the unique local contexts and requirements are not adequately considered. In this study, we evaluate the feasibility of utilizing models developed in the United Kingdom (a HIC) within hospitals in Vietnam (a LMIC). Consequently, we present and discuss practical methodologies aimed at improving model performance, emphasizing the critical importance of tailoring solutions to the distinct healthcare systems found in LMICs. Our findings emphasize the necessity for collaborative initiatives and solutions that are sensitive to the local context in order to effectively tackle the healthcare challenges that are unique to these regions.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.2MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-024-52618-6
Authors
- Publisher:
- Springer Nature
- Journal:
- Nature Communications More from this journal
- Volume:
- 15
- Article number:
- 8270
- Publication date:
- 2024-09-27
- Acceptance date:
- 2024-09-17
- DOI:
- EISSN:
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2041-1723
- Language:
-
English
- Pubs id:
-
2031462
- Local pid:
-
pubs:2031462
- Deposit date:
-
2024-09-21
- ARK identifier:
Terms of use
- Copyright holder:
- Yang et al
- Copyright date:
- 2024
- Rights statement:
- ©2024 The Authors. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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