AI Article Synopsis
- Collaborative AI partnerships between high-income countries (HICs) and low- to middle-income countries (LMICs) are becoming more frequent, mainly due to resource challenges in LMICs.
- Ensuring fairness and equity in these collaborations is critical, as differences between HIC and LMIC hospitals can impact performance outcomes.
- The study, through a COVID-19 screening case study, shows that using bias mitigation methods in algorithms can enhance outcome fairness and maintain diagnostic accuracy across both settings.
Article Abstract
Collaborative efforts in artificial intelligence (AI) are increasingly common between high-income countries (HICs) and low- to middle-income countries (LMICs). Given the resource limitations often encountered by LMICs, collaboration becomes crucial for pooling resources, expertise, and knowledge. Despite the apparent advantages, ensuring the fairness and equity of these collaborative models is essential, especially considering the distinct differences between LMIC and HIC hospitals. In this study, we show that collaborative AI approaches can lead to divergent performance outcomes across HIC and LMIC settings, particularly in the presence of data imbalances. Through a real-world COVID-19 screening case study, we demonstrate that implementing algorithmic-level bias mitigation methods significantly improves outcome fairness between HIC and LMIC sites while maintaining high diagnostic sensitivity. We compare our results against previous benchmarks, utilizing datasets from four independent United Kingdom Hospitals and one Vietnamese hospital, representing HIC and LMIC settings, respectively.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164855 | PMC |
| http://dx.doi.org/10.1038/s41598-024-64210-5 | DOI Listing |
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