Exploring potential and pitfalls of AI in health research in low- and middle-income countries, indicating significant implications for equity and healthcare delivery.
The rapid expansion of artificial intelligence (AI) in health research has generated both considerable enthusiasm and important concerns. While AI offers new opportunities to accelerate discovery, improve prediction, and optimise health system performance, its growing influence also raises critical questions about equity, validity, and real-world applicability, particularly in low- and middle-income countries (LMICs).1,2 In these settings, where structural constraints shape both data availability and healthcare delivery, the implications of adopting AI are especially consequential.
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Gumede et al. (2026) studied this question.
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