The Algorithmic Blind Spot: Why Artificial Intelligence Needs Data from LMICs – A Call for Papers

Brain health is an emerging global challenge, which has strong implications for the development and implementation of artificial intelligence (AI) in medicine. To date, advances in medicine driven by data from high income countries have shaped everything downstream: what conditions are studied, what populations are enrolled, what tools are validated. This is a problem beyond simple underrepresentation; it is a problem of algorithmic drift. The algorithms generated by AI will not be adequately trained for lower-and middle-income country (LMIC) scenarios unless data from LMICs make their way into the algorithm. Bridging this algorithmic gap requires publication of locally generated, interpreted, and context-specific evidence from LMICs to ensure representation from the majority of the global population.

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Publication Details

Journal
Journal of Brain Health
Published
2026-05-18
DOI
https://doi.org/10.65104/001c.162204
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

The Algorithmic Blind Spot: Why Artificial Intelligence Needs Data from LMICs – A Call for Papers

Kathleen M Hayden
Journal of Brain Health
Artificial Intelligence in Healthcare and Education
article

The Algorithmic Blind Spot: Why Artificial Intelligence Needs Data from LMICs – A Call for Papers

Kathleen M Hayden
article en

Abstract

Brain health is an emerging global challenge, which has strong implications for the development and implementation of artificial intelligence (AI) in medicine. To date, advances in medicine driven by data from high income countries have shaped everything downstream: what conditions are studied, what populations are enrolled, what tools are validated. This is a problem beyond simple underrepresentation; it is a problem of algorithmic drift. The algorithms generated by AI will not be adequately trained for lower-and middle-income country (LMIC) scenarios unless data from LMICs make their way into the algorithm. Bridging this algorithmic gap requires publication of locally generated, interpreted, and context-specific evidence from LMICs to ensure representation from the majority of the global population.

Journal of Brain HealthVol. 1(1)
Trinity College Dublin (IE)
No poverty
Openalex Percentile: Top 7%
Artificial Intelligence in Healthcare and Education
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