Global Health Roundtable: AI Qualitative Research

According to Ten simple rules for a successful global health roundtable, global health roundtables are structured discussions that bring researchers, practitioners, policymakers, community representatives, and people with lived experience together to exchange knowledge and address complex health challenges.

The 2026 PLOS article presents ten rules covering topic selection, objectives, participant diversity, outputs, commitment, inclusion, power dynamics, flexibility, future engagement, and ethical recognition. AI-enabled qualitative research can help organize the resulting transcripts and documents, but the PLOS authors emphasize that roundtable quality depends on deliberate design and equitable participation.

• The PLOS authors published the ten-rule framework on November 5, 2026, in PLoS Computational Biology, volume 22, issue 11.
• The PLOS article defines a global health roundtable as a structured and facilitated discussion involving stakeholders from diverse geographical and institutional contexts.
• The PLOS authors distinguish roundtable outputs from interviews and focus groups, noting that roundtables can provide broader cross-sector insight while offering less depth on individual or localized experiences.
• The PLOS authors state that roundtables should be treated as relational processes, not merely one-off events, because follow-up can support sustained collaboration and knowledge exchange.
• The PLOS authors write that "Roundtables are not scripted performances but dynamic, evolving conversations, " making flexible analysis important when discussions move beyond the original agenda.

The PLOS authors argue that global health roundtables complement interviews and focus groups by creating a longer-term, integrative platform for collective dialogue. The authors developed their ten rules from collective experience designing, convening, facilitating, and evaluating roundtables across global health research, policy, and practice settings.

The framework begins before data collection. According to the PLOS authors, organizers should select a focused knowledge gap, define a clear objective, create purposeful discussion questions, and invite participants with diverse expertise. These design choices determine whether later qualitative analysis captures meaningful differences across professional, geographic, and lived-experience perspectives.

The PLOS authors identify inclusion and power management as methodological requirements, not optional additions. The framework recommends creating space for community representatives, frontline workers, caregivers, people with lived experience, clinicians, academics, administrators, and policymakers while preventing senior or high-status participants from dominating the conversation.

The PLOS authors also recommend defining intended outputs early. Possible outputs include policy briefs, guidelines, health reports, peer-reviewed articles, white papers, memorandums of understanding, steering committees, and advocacy initiatives.

The PLOS authors caution that roundtable-generated outputs may provide less individual or localized depth than interviews or focus groups. Researchers should therefore use AI analysis to support comparison, synthesis, and auditability without presenting broad discussion themes as a substitute for detailed personal narratives.

Global health researchers should treat AI-enabled qualitative research as an augmentation layer around inclusive roundtable practice, not as a replacement for participation or facilitation. The PLOS authors’ framework makes clear that the quality of interpretation depends on who is included, how power is managed, and how contributors are recognized.

For roundtable preparation, researchers can use document analysis to compare background papers, policy reports, prior studies, and stakeholder plans. A structured review can identify recurring knowledge gaps, conflicting assumptions, and underrepresented perspectives before discussion questions are finalized.

For roundtable analysis, researchers can combine thematic analysis with cross-segment comparisons. A useful workflow is to compare themes by stakeholder role, region, language, institution, or lived-experience status, then return to the underlying excerpts to check whether the interpretation is supported.

For follow-up, researchers can connect transcripts with meeting notes, policy briefs, action plans, and later correspondence. The PLOS authors recommend treating roundtables as catalysts for future engagement, and a connected qualitative evidence base can make unresolved questions, commitments, and changes in priorities easier to track.

Ethics note: AI-assisted analysis in global health research should remain non-diagnostic, protect participant privacy, preserve contributor recognition, and require human review of culturally or politically sensitive interpretations.

The 2026 PLOS framework shows that successful global health roundtables require clear objectives, diverse participation, active power management, defined outputs, sustained engagement, and ethical recognition of contributions. AI-enabled qualitative research can strengthen this process by making transcripts, documents, stakeholder segments, and follow-up outputs easier to analyze together.

The strongest workflow combines human-led facilitation with AI-assisted transcription, translation, thematic analysis, cross-segment comparison, and evidence retrieval. The approach preserves the relational and participatory character of roundtables while reducing the manual burden of synthesis.

Research teams that want to turn global health roundtable data into traceable, actionable findings can try Evidano for free.