To outline approaches for designing clinician-facing AI tools that advance health equity in resource-constrained low- and middle-income countries (LMICs), emphasizing socio-technical considerations.
Key Findings:
AI has transformative potential in LMICs by improving access to health services and reducing disparities, but diverse datasets are crucial for effective implementation.
Most AI evaluation studies are conducted in high-income countries, limiting global contributions to equitable health outcomes.
A problem-driven approach helps refocus AI development on specific clinical needs rather than solely on technology.
Interpretation:
Integrating socio-technical considerations in AI design can enhance healthcare quality and safety, particularly in resource-constrained environments, by ensuring that AI tools are contextually relevant and user-friendly.
Limitations:
Limited literature on clinician-facing AI tools in resource-constrained settings.
Challenges in aligning AI tool design with the operational realities of LMICs, including potential biases in AI algorithms due to limited data diversity.
Conclusion:
Adopting a socio-technical lens and a problem-driven approach is essential for developing effective AI solutions that promote health equity in LMICs, addressing the urgent need for equitable healthcare access.
