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Related Experiment Videos

Human-AI Interaction in Low- and Middle-Income Countries: Qualitative Study of How Local Human Factors Influence AI

Evangelia Baka1, Niklas Krischer2, Udani De Silva3

  • 1Virtual Medicine Centre, University Hospital of Geneva, Geneva, Switzerland.

JMIR AI
|June 3, 2026
PubMed
Summary

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

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Developing artificial intelligence (AI) in low- and middle-income countries requires human involvement and local adaptation. Co-designing AI with communities ensures cultural relevance and promotes social equity.

Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Global Health

Background:

  • Artificial intelligence (AI) offers transformative potential in healthcare and research.
  • Ethical, societal, and regulatory challenges of AI are amplified in low- and middle-income countries (LMICs).
  • A gap exists in qualitative research on human involvement and sociocultural dynamics in AI development within LMICs.

Purpose of the Study:

  • To explore human involvement across the AI lifecycle in LMICs.
  • To examine how cultural, societal, and governance factors shape AI perceptions and expectations.
  • To identify critical needs for effective AI deployment in diverse global contexts.

Main Methods:

  • Conducted 21 qualitative online interviews with AI researchers and innovators.
Keywords:
AI ethicsAI governanceLMICscultural adaptationhuman-AI collaborationlow- and middle-income countriesparticipatory designresponsible AI

Related Experiment Videos

  • Covered regions including the Middle East and North Africa (MENA), Africa, Latin America, and Asia.
  • Employed a semistructured interview guide based on the Knowledge, Attitude, and Practice (KAP) framework and thematic analysis.

    • Main Results:

    • Identified five key themes: human oversight, AI ethics training, locally tailored AI systems, human-centered AI governance, and multidisciplinary teams.
    • Highlighted significant gaps in AI literacy, ethical governance, and interdisciplinary collaboration.
    • Emphasized the necessity of co-designing AI solutions with local communities for cultural and contextual relevance.

    Conclusions:

    • Underscored the urgent need for participatory AI development in LMICs.
    • Called for investment in AI education, ethical oversight, and inclusive governance.
    • Advocated for AI to be a tool for social equity, not exclusion.