Global Health Roundtables: AI Qualitative Research

According to Ten simple rules for a successful global health roundtable, a successful global health roundtable requires a focused topic, a clearly defined objective, diverse participation, inclusive facilitation, ethical recognition, and planned follow-up. AI-enabled qualitative research can help analysts organize and compare the resulting transcripts and documents, but human researchers must retain responsibility for interpretation, consent, and decisions about whose voices are represented.

• On November 5, 2026, Maria Gabriela Uribe Guajardo and co-authors published ten rules for designing, facilitating, and using global health roundtables.
• The authors define a global health roundtable as a structured and facilitated discussion involving researchers, practitioners, policymakers, community representatives, and people with lived experience from diverse contexts.
• The PLOS article states that roundtables can produce academic and nonacademic outputs, including policy briefs, guidelines, reports, white papers, and advocacy initiatives.
• The authors caution that roundtable outputs may provide less individual or localized depth than interviews or focus groups.
• AI-enabled qualitative analysis is most useful after a roundtable when researchers need to trace themes, compare stakeholder groups, and connect conclusions to supporting excerpts.

Global health roundtable qualitative research works best when the discussion is designed as an ongoing knowledge and collaboration process rather than as a one-time event. According to the PLOS authors, roundtables complement interviews and focus groups by bringing multiple forms of knowledge into a shared setting, including experimental, observational, contextual, applied, and experiential knowledge.

The authors' framework contains ten rules published on November 5, 2026. The rules cover topic selection, objectives and questions, participant diversity, planned outputs, attendance and commitment, inclusive participation, power dynamics, openness to unexpected discussion, future engagement, and recognition of contributions.

The authors describe the roundtable as a flexible format, but they do not present flexibility as a substitute for structure. The PLOS article recommends a clear agenda and detailed run sheet while allowing discussion to evolve when new or unexpected insights emerge.

The authors also state, "Roundtables are not scripted performances but dynamic, evolving conversations." That observation matters for analysis because the most important themes may emerge through disagreement, reframing, or interaction between stakeholder perspectives rather than through responses to a single planned question.

The authors further emphasize that inclusive design must address power. People with lived experience, caregivers, frontline workers, and community representatives may speak less freely alongside senior clinicians, policymakers, or institutional leaders. A transcript can show who spoke, but interpretation still requires attention to context, facilitation, silence, and unequal authority.

Global health researchers should treat AI as an aid for evidence organization, not as an autonomous decision-maker about meaning, inclusion, or ethics. The PLOS authors write that successful roundtables require "deliberate planning, ethical reflection, and active facilitation, " and those requirements continue after the conversation ends.

Researchers can apply AI-enabled qualitative research to four practical tasks:

• Researchers can transcribe and clean multilingual roundtable recordings so analysts can search spoken contributions alongside notes, reports, and policy documents.
• Researchers can identify recurring themes such as health-system access, equity, governance, implementation barriers, or community trust without losing the excerpts that support each theme.
• Researchers can compare themes across stakeholder groups, regions, professional roles, or rounds in a serial engagement program.
• Researchers can use frequency and co-occurrence views to identify language patterns, while treating frequency as a prompt for interpretation rather than proof of importance.
• Researchers can ask targeted questions across the complete evidence set, such as which recommendations were raised by community representatives and whether later sessions addressed those recommendations.

According to the PLOS authors, roundtables can support co-production and translation of knowledge, but the quality of that outcome depends on participation and power-sharing. AI analysis should therefore preserve speaker identity where ethically appropriate, record the provenance of excerpts, and make it possible for researchers to inspect how a theme was generated.

Global health teams should also plan analysis outputs before the event. The authors recommend clarifying the intended outputs, assigning responsibility for writing and coordination, and communicating authorship or recognition expectations to participants early.

Global health roundtable qualitative research can produce more useful and equitable findings when researchers connect careful facilitation with transparent analysis. The ten rules published by the PLOS authors on November 5, 2026, provide a practical framework for defining purpose, widening participation, managing power, and sustaining collaboration.

AI-enabled qualitative research can reduce the mechanical burden of transcription and synthesis while helping teams compare themes across languages, stakeholder groups, regions, and sessions. The strongest workflow keeps human researchers accountable for interpretation, ethics, participant recognition, and decisions about how findings are used.

Evidano gives global health teams one workspace for roundtable transcripts, supporting documents, thematic analysis, cross-segment comparisons, and evidence-focused questions. Try Evidano for free to turn complex qualitative data into a more organized, reviewable evidence base.