This theme invites submissions that present real-world deployment of responsible AI technologies for epidemic or pandemic preparedness and broader health system challenges in the Global South.

Suggested components for submissions:

● Health challenge context: Clearly describe the public health problem being addressed (e.g., disease surveillance gaps, healthcare system overload, outbreak response delays), why it matters, and its impact on specific populations or health outcomes.

● AI intervention: Detail the type of AI solution implemented—machine learning, computer vision, NLP, predictive modeling, etc.—and how it was adapted to the local context.

● Implementation environment: Explain where and how the AI solution was deployed (e.g., hospital, community, digital health platform), and what infrastructure and capacity supported its use.

● Policy and data challenges: Address challenges around regulatory environments, ethical approval, data access, interoperability, or limited digital infrastructure.

● Global South leadership: Emphasize how the innovation or deployment was led by researchers, communities, or institutions in the Global South.

● Equity and inclusion: Showcase how the intervention considered gender, disability, language, or socio-economic barriers.

● Outcomes: Provide evidence of effectiveness (quantitative or qualitative), user feedback, improved health outcomes, or lessons learned for scale-up.

This theme focuses on comparative studies that analyze experiences, lessons, or models across two or more countries or regions within the Global South. Each subtopic below provides a lens for such comparative exploration.

The subtopics below can be mixed and matched by authors based on the evidence gaps they wish to fill:

● Community engagement: Explore frameworks, strategies, or case comparisons of how community voices were incorporated in the design, testing, and implementation of AI for health. Discuss participatory design, trust-building, co-creation, or challenges in community mobilization.

● Gender, disability, and equity: Compare how gender, disability, and other axes of marginalization were considered across different AI health interventions. What frameworks or indicators were used? What trade-offs or gaps emerged in addressing inclusivity in the AI pipeline?

● Ethics and governance: Analyze how ethical concerns and governance mechanisms were managed across different contexts—e.g., data ownership, informed consent, algorithmic transparency, or accountability structures. Highlight gaps or best practices in the Global South.

● Data challenges: Discuss common data-related barriers and enablers in AI implementation—e.g., data quality, standardization, bias, missing data, language limitations—and how they were addressed in different settings.

● Scaling AI responsibly: Examine how AI projects in different countries approached scaling—what strategies were used to expand geographic reach, integrate with national systems, or institutionalize innovations. What lessons can be shared?

● Digital literacy and AI tool adoption: Compare readiness of healthcare systems and providers to adopt AI tools. Were there digital skill gaps, mistrust, or resistance? What capacity-building efforts helped with adoption?

● Social data in AI models: Analyze how non-clinical, social determinants of health data (e.g., mobility, economic conditions, behaviors) were used in AI models for pandemic preparedness, and how this varied across settings.

● Conversational AI in healthcare: Explore how AI chatbots or voice assistants were used for public health messaging, triage, patient monitoring, etc. How effective were they in different languages, literacy levels, or trust environments?

● Climate data in AI models: Compare how AI models integrated climate variables—e.g., rainfall, temperature, air quality—to predict disease outbreaks or health impacts, especially for climate-sensitive diseases (e.g., malaria, dengue).

This theme features early-stage research projects that are still in development but show strong potential for real-world deployment in epidemic or pandemic preparedness. Authors should explain the problem being addressed, stage of development, expected implementation pathway, and any collaborations or community engagement initiated.

Submissions may include:

● Pilot studies or prototypes for AI-enabled tools in disease detection, outbreak prediction, or resource planning.

● Preliminary modeling work with synthetic, small-scale, or limited data.

● Lab-based AI solutions developed with future public health application in mind.

● Evidence of relevance, such as potential to address current surveillance or preparedness gaps in the Global South.

● Feasibility studies or proof-of-concept experiments tied to real-world implementation goals.