Cultural Insight: AI Qualitative Analysis of Leprosy

Researchers should code local terminology separately because the study found that kokobe and gwasi described visible symptoms and social meanings that did not exactly correspond to the biomedical category of leprosy.

The study reported that one mother did not recognize the French terms lèpre or maladie de Hansen but understood the local terms gwasi and kokobe. A qualitative analysis workflow should therefore preserve original wording, translated wording, speaker context, and the researcher's interpretation as distinct fields.

Qualitative analysis can reveal healthcare barriers by connecting narratives about diagnosis, transport, cost, treatment, disclosure, and follow-up into themes and sub-themes.

The 2026 study described prolonged diagnostic pathways, transport problems, financial barriers, treatment interruptions, and fear of stigma. Cross-segment analysis can help health teams compare these barriers across cultural groups, locations, and patient experiences without reducing the findings to a single frequency count.

Researchers should analyze apparently conflicting beliefs as part of the care context rather than automatically treating them as data errors.

The study found that participants could use spiritual or environmental explanations while also viewing biomedical treatment as effective. One community member summarized this coexistence by saying, "Both conventional and traditional medicine can be used, provided the treatment is taken properly, " according to the study.

Sensitive interview data should be analyzed with strict access controls, pseudonymization, and careful reporting because the study authors stated that the transcripts could not be publicly shared without risking participant identification.

The study authors stored the transcripts securely at the French Guiana University Hospital and withheld them from public repositories because the disease is rare and some communities are small. Researchers using AI should apply the same principle of data minimization and confidentiality to uploaded transcripts.

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interview transcripts, documents, field notes, and survey responses.

Evidano can organize qualitative material into themes, sub-themes, frequencies, and cross-segment comparisons. For a study like the French Guiana research, researchers could compare patient and community interviews, examine cultural groups separately, and review how terms such as kokobe and gwasi connect to stigma, symptoms, and care pathways.

Researchers can review Evidano's qualitative outputs through Evidano's analysis features while retaining human responsibility for interpretation and culturally sensitive conclusions.

Multilingual qualitative datasets require transcription and translation workflows that preserve meaning, speaker context, and local vocabulary.

Evidano supports transcription with custom dictionaries and PII redaction, plus translation with custom dictionaries. Custom terminology can help researchers preserve domain-specific expressions and compare original and translated language during analysis.

Complex cultural findings become easier to inspect when researchers can view relationships among codes, subcodes, and recurring terms.

Evidano provides visualizations such as word clouds, co-occurrence networks, and hierarchical code-to-subcode structures. These views could help a research team inspect relationships among local terminology, explanatory models, stigma, treatment adherence, and community support before validating the interpretation against the source transcripts.

Sensitive health research requires encryption, controlled access, and clear limits on secondary data use.

Evidano encrypts data and does not use customer data to train third-party models, which is relevant for research involving rare diseases, identifiable narratives, or small communities. Researchers should still obtain appropriate consent, remove unnecessary identifiers, and follow their institution's ethics and data governance requirements.