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Tag: neglected tropical diseases

  • A global health framework for Artificial Intelligence as co-worker to support networked learning and local action

    A global health framework for Artificial Intelligence as co-worker to support networked learning and local action

    The theme of International Education Day 2025, “AI and education: Preserving human agency in a world of automation,” invites critical examination of how artificial intelligence might enhance rather than replace human capabilities in learning and leadership. Global health education offers a compelling context for exploring this question, as mounting challenges from climate change to persistent inequities demand new approaches to building collective capability.

    The promise of connected communities

    Recent experiences like the Teach to Reach initiative demonstrate the potential of structured peer learning networks. The platform has connected over 60,000 health workers, primarily government workers from districts and facilities across 82 countries, including those serving in conflict zones, remote rural areas, and urban settlements. For example, their exchanges about climate change impacts on community health point the way toward more distributed forms of knowledge creation in global health. 

    Analysis of these networks suggests possibilities for integrating artificial intelligence not merely as tools but as active partners in learning and action. However, realizing this potential requires careful attention to how AI capabilities might enhance rather than disrupt the human connections that drive current success.

    Artificial Intelligence (AI) partnership could provide crucial support for tackling mounting challenges. More importantly, they could help pioneer new approaches to learning and action that genuinely serve community needs while advancing our understanding of how human and machine intelligence might work together in service of global health.

    Understanding Artificial Intelligence (AI) as partner, not tool

    The distinction between AI tools and AI partners merits careful examination. Early AI applications in global health primarily automate existing processes – analyzing data, delivering content, or providing recommendations. While valuable, this tool-based approach maintains clear separation between human and machine capabilities.

    AI partnership suggests a different relationship, where artificial intelligence participates actively in learning networks alongside human practitioners. This could mean AI systems that:

    • Engage in dialogue with health workers about local observations
    • Help validate emerging insights through pattern analysis
    • Support adaptation of solutions across contexts
    • Facilitate connections between practitioners facing similar challenges

    The key difference lies in moving from algorithmic recommendations to collaborative intelligence that combines human wisdom with machine capabilities.

    A framework for AI partnership in global health

    Analysis of current peer learning networks suggests several dimensions where AI partnership could enhance collective capabilities:

    • Knowledge creation: Current peer learning networks enable health workers to share observations and experiences across borders. AI partners could enrich this process by engaging in dialogue about patterns and connections, while preserving the central role of human judgment in validating insights.
    • Learning process: Teach to Reach demonstrates how structured peer learning accelerates knowledge sharing and adaptation. AI could participate in these networks by contributing additional perspectives, supporting rapid synthesis of experiences, and helping identify promising practices.
    • Local leadership: Health workers develop and implement solutions based on deep understanding of community needs. AI partnership could enhance decision-making by exploring options, modeling potential outcomes, and validating approaches while maintaining human agency.
    • Network formation: Digital platforms currently enable lateral connections between health workers across regions. AI could actively facilitate network development by identifying valuable connections and supporting knowledge flow across boundaries.
    • Implementation support: Peer review and structured feedback drive current learning-to-action cycles. AI partners could engage in ongoing dialogue about implementation challenges while preserving the essential role of human judgment in local contexts.
    • Evidence generation: Networks document experiences and outcomes through structured processes. AI collaboration could help develop and test hypotheses about effective practices while maintaining focus on locally-relevant evidence.

    Applications across three global health challenges

    This framework suggests new possibilities for addressing persistent challenges.

    1. Immunization systems

    Current global immunization goals face significant obstacles in reaching zero-dose children and strengthening routine services. AI partnership could enhance efforts by:

    • Supporting microplanning by mediating dialogue about local barriers
    • Facilitating rapid learning about successful engagement strategies
    • Enabling coordinated action across health system levels
    • Modeling potential impacts of different intervention approaches

    2. Neglected Tropical Diseases (NTDs)

    The fight against NTDs suffers from critical information gaps and weak coordination at local levels. Many communities, including health workers, lack basic knowledge about these diseases. AI partnership could help address these gaps through:

    • Facilitating knowledge flow between affected communities
    • Supporting coordination of control efforts
    • Enabling rapid validation of successful approaches
    • Strengthening surveillance and response networks

    3. Climate change and health

    Health workers’ observations of climate impacts on community health provide crucial early warning of emerging threats. AI partnership could enhance response capability by:

    • Engaging in dialogue about changing disease patterns
    • Supporting rapid sharing of adaptation strategies
    • Facilitating coordinated action across regions
    • Modeling potential impacts of interventions

    Pandemic preparedness beyond early warning

    The experience of digital health networks during recent disease outbreaks reveals both the power of distributed response capabilities and the potential for enhancement through AI partnership. When COVID-19 emerged, networks of health workers demonstrated remarkable ability to rapidly share insights and adapt practices. For example, the Geneva Learning Foundation’s COVID-19 Peer Hub connected over 6,000 frontline health professionals who collectively generated and implemented recovery strategies at rates seven times faster than isolated efforts.

    This networked response capability suggests new possibilities for pandemic preparedness that combines human and machine intelligence. Heightened preparedness could emerge from the interaction between health workers, communities, and AI partners engaged in continuous learning and adaptation.

    Current pandemic preparedness emphasizes early detection through formal surveillance. However, health workers in local communities often observe concerning patterns before these register in official systems.

    AI partnership could enhance this distributed sensing capability while maintaining its grounding in local realities. Rather than simply analyzing reports, AI systems could engage in ongoing dialogue with health workers about their observations, helping to:

    • Explore possible patterns and connections
    • Test hypotheses about emerging threats
    • Model potential trajectories
    • Identify similar experiences across regions

    The key lies in combining human judgment about local significance with AI capabilities for pattern recognition across larger scales.

    The focus remains on accelerating locally-led learning rather than imposing standardized solutions.

    Perhaps most importantly, AI partnership could enhance the collective intelligence that emerges when practitioners work together to implement solutions. Current networks enable health workers to share implementation experiences and adapt strategies to local contexts. Adding AI capabilities could support this through:

    • Ongoing dialogue about implementation challenges
    • Analysis of patterns in successful adaptation
    • Support for rapid testing of modifications
    • Facilitation of cross-context learning

    Success requires maintaining human agency in implementation while leveraging machine capabilities to strengthen collective problem-solving.

    This networked vision of pandemic preparedness, enhanced through AI partnership, represents a fundamental shift from current approaches. Rather than attempting to predict and control outbreaks through centralized systems, it suggests building distributed capabilities for continuous learning and adaptation. The experience of existing health worker networks provides a foundation for this transformation, while artificial intelligence offers new possibilities for strengthening collective response capabilities.

    Investment for innovation

    Realizing this vision requires strategic investment in:

    • Network development: Supporting growth of peer learning platforms that accelerate local action while maintaining focus on human connection.
    • AI partnership innovation: Developing systems designed to participate in learning networks while preserving human agency.
    • Implementation research: Studying how AI partnership affects collective capabilities and health outcomes.
    • Capacity strengthening: Building health worker capabilities to effectively collaborate with AI while maintaining critical judgment.

    Looking forward

    The transformation of global health learning requires moving beyond both conventional practices of technical assistance and simple automation. Experience with peer learning networks demonstrates what becomes possible when health workers connect to share knowledge and drive change.

    Adding artificial intelligence as partners in these networks – rather than replacements for human connection – could enhance collective capabilities to protect community health. However, success requires careful attention to maintaining human agency while leveraging technology to strengthen rather than supplant local leadership.

    7 key principles for AI partnership

    1. Maintain human agency in decision-making
    2. Support rather than replace local leadership
    3. Enhance collective intelligence
    4. Enable rapid learning and adaptation
    5. Preserve context sensitivity
    6. Facilitate knowledge flow across boundaries
    7. Build sustainable learning systems

    Listen to an AI-generated podcast about this article

    🤖 This podcast was generated by AI, discussing Reda Sadki’s 24 January 2025 article “A global health framework for Artificial Intelligence as co-worker to support networked learning and local action”. While the conversation is AI-generated, the framework and examples discussed are based on the published article.

    Framework: AI partnership for learning and local action in global health

    DimensionCurrent StateAI as ToolsAI as PartnersPotential Impact
    Knowledge creationHealth workers share observations and experiences through peer networksAI analyzes patterns in shared dataAI engages in dialogue with health workers, asking questions, suggesting connections, validating insightsNew forms of collective intelligence combining human and machine capabilities
    Learning processStructured peer learning through digital platforms and networksAI delivers content and analyzes performanceAI participates in peer learning networks, contributes insights, supports adaptationAccelerated learning through human-AI collaboration
    Local leadershipHealth workers develop and implement solutions for community challengesAI provides recommendations based on data analysisAI works alongside local leaders to explore options, model scenarios, validate approachesEnhanced decision-making combining local wisdom with AI capabilities
    Network formationLateral connections between health workers across regionsAI matches similar profiles or challengesAI actively facilitates network development, identifies valuable connectionsMore effective knowledge networks leveraging both human and machine intelligence
    Implementation supportPeer review and structured feedback on action plansAI checks plans against best practicesAI engages in iterative dialogue about implementation challenges and solutionsImproved implementation through combined human-AI problem-solving
    Evidence generationDocumentation of experiences and outcomes through structured processesAI analyzes implementation dataAI collaborates with health workers to develop and test hypotheses about what worksNew approaches to generating practice-based evidence

    Image: The Geneva Learning Foundation Collection © 2024

  • Klepac and colleagues‘ scoping review of climate change, malaria and neglected tropical diseases: what about the epistemic significance of health worker knowledge?

    Klepac and colleagues‘ scoping review of climate change, malaria and neglected tropical diseases: what about the epistemic significance of health worker knowledge?

    By Luchuo E. Bain and Reda Sadki

    The scoping review by Klepac et al. provides a comprehensive overview of codified academic knowledge about the complex interplay between climate change and a wide range of infectious diseases, including malaria and 20 neglected tropical diseases (NTDs).

    The review synthesized findings from 511 papers published between 2010 and 2023, revealing that the vast majority of studies focused on malaria, dengue, chikungunya, and leishmaniasis, while other NTDs were relatively understudied.

    The geographical distribution of studies also varied, with malaria studies concentrated in Africa, Brazil, China, and India, and dengue and chikungunya studies more prevalent in Australia, China, India, Europe, and the USA.

    One of the most striking findings of the review is the potential for climate change to have profound and varied effects on the distribution and transmission of malaria and NTDs, with impacts likely to vary by disease, location, and time.

    However, the authors also highlight the uncertainty surrounding the overall global impact due to the complexity of the interactions and the limitations of current predictive models.

    This underscores the need for more comprehensive, collaborative, and standardized modeling efforts to better understand the direct and indirect effects of climate change on these diseases.

    Another significant insight from the review is the relative lack of attention given to climate change mitigation and adaptation strategies in the existing literature.

    Only 34% of the included papers considered mitigation strategies, and a mere 5% addressed adaptation strategies.

    Could we imagine future mapping to recognize the value of new mechanisms for and actors of knowledge production that do not meet the conventional criteria for what currently counts as valid knowledge?

    What might be the return on going at least one step further beyond questioning our own underlying assumptions about ‘how science is done’ to actually supporting and investing in innovative indigenous- and community-led, co-created initiatives?

    This gap highlights the urgent need for more research on how to effectively reduce the impact of climate change on malaria and NTDs, particularly in areas with the highest disease burdens and the populations most vulnerable to the impacts of climate change.

    While the review emphasizes the need for more research to fill these evidence gaps, this begs the question of the resources and time required to fill them.

    This is where there is likely to be value in the experiential data from health workers on the frontlines to provide insights into the mechanisms of climate change impacts on health and effective response strategies.

    The upcoming Teach to Reach 10 event (background | registration) , a massive open peer learning platform that brings together health professionals from around the world to network and learn from each other’s experiences, offers a unique opportunity to engage thousands of health workers in a dialogue that can deepen our understanding of how climate change is affecting the health of local communities.

    Experiential data has been, historically, dismissed as ‘anecdotal’ evidence at best.

    The value and significance of what you know because you are there every day, serving the health of your community, has been ignored.

    The expertise and knowledge of frontline health workers are often overlooked or undervalued in global health decision-making processes, despite their critical role in delivering health services and their deep understanding of local contexts and challenges.

    Yes, the importance of incorporating the insights and experiences of health workers in the global health discourse cannot be overstated.

    As Abimbola and Pai (2020) argue, the decolonization of global health requires a shift towards valuing and amplifying the voices of those who have been historically marginalized and excluded from the dominant narratives.

    This concept, known as epistemic justice, recognizes that knowledge is not solely the domain of academic experts but is also held by those with lived experiences and practical expertise (Fricker, 2007).

    Epistemic injustice, as defined by Fricker (2007), occurs when an individual is wronged in their capacity as a knower, either through testimonial injustice (when a speaker’s credibility is undervalued due to prejudice) or hermeneutical injustice (when there is a gap in collective understanding that disadvantages certain groups).

    In the context of global health, epistemic injustice often manifests in the marginalization of knowledge held by communities and health workers in low- and middle-income countries, as well as the dominance of Western biomedical paradigms over local ways of knowing (Bhakuni & Abimbola, 2021).

    By engaging health workers from around the world in peer learning and knowledge sharing, Teach to Reach can help to challenge the epistemic injustice that has long plagued global health research and practice.

    By providing a platform for health workers to share their experiences and insights, Teach to Reach – alongside many other initiatives focused on listening to and learning from communities – can contribute to ensuring that the fight against malaria and NTDs in the face of climate change is informed not only by rigorous scientific evidence but also by the practical wisdom of those on the ground.

    That is only if global partners are willing to challenge their own assumptions, and take the time to listen and learn.

    Moreover, the decolonization of global health requires a shift towards more equitable and inclusive forms of knowledge production and dissemination.

    This involves challenging the historical legacies of colonialism and racism that have shaped the global health field, as well as the power imbalances that continue to privilege certain forms of knowledge over others (Büyüm et al., 2020).

    By fostering a dialogue between health workers and global partners, Teach to Reach can help to bridge the gap between research and practice, ensuring that the latest scientific findings are effectively translated into actionable strategies that are grounded in local realities and responsive to the needs of those most affected by climate change and infectious diseases.

    The value of experiential data from health workers in filling evidence gaps and informing effective response strategies cannot be understated.

    As the Klepac review highlights, there is a paucity of research on the impacts of climate change on many NTDs and the effectiveness of mitigation and adaptation strategies.

    While more rigorous scientific studies are undoubtedly needed, waiting years or decades for this evidence to accumulate before taking action is not a viable option given the urgency of the climate crisis and its devastating impacts on health.

    Health workers’ firsthand observations and experiences can provide valuable insights into the complex mechanisms through which climate change is affecting the distribution and transmission of malaria and NTDs, as well as the effectiveness of different intervention strategies in real-world settings.

    This type of contextual knowledge is essential for developing locally tailored solutions that account for the unique social, cultural, and environmental factors that shape disease dynamics in different communities.

    Furthermore, engaging health workers as active partners in research and decision-making processes can help to ensure that the solutions developed are not only scientifically sound but also feasible, acceptable, and sustainable in practice.

    The involvement of frontline health workers in the co-creation of knowledge and interventions can lead to more effective, equitable, and context-specific solutions that are responsive to the needs and priorities of local communities.

    References

    Abimbola, S., & Pai, M. (2020). Will global health survive its decolonisation? The Lancet, 396(10263), 1627-1628. https://doi.org/10.1016/S0140-6736(20)32417-X

    Bhakuni, H., & Abimbola, S. (2021). Epistemic injustice in academic global health. The Lancet Global Health, 9(10), e1465-e1470. https://doi.org/10.1016/S2214-109X(21)00301-6

    Büyüm, A. M., Kenney, C., Koris, A., Mkumba, L., & Raveendran, Y. (2020). Decolonising global health: If not now, when? BMJ Global Health, 5(8), e003394. https://doi.org/10.1136/bmjgh-2020-003394

    Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.

    Klepac, P., et al., 2024. Climate change, malaria and neglected tropical diseases: a scoping review. Transactions of The Royal Society of Tropical Medicine and Hygiene. https://doi.org/10.1093/trstmh/trae026