Treffer: Advancing AI-driven thematic analysis in qualitative research: a comparative study of nine generative models on Cutaneous Leishmaniasis data.

Background: As part of qualitative research, the thematic analysis is time-consuming and technical. The rise of generative artificial intelligence (A.I.), especially large language models, has brought hope in enhancing and partly automating thematic analysis.
Methods: The study assessed the relative efficacy of conventional against AI-assisted thematic analysis when investigating the psychosocial impact of cutaneous leishmaniasis (CL) scars. Four hundred forty-eight participant responses from a core study were analysed comparing nine A.I. generative models: Llama 3.1 405B, Claude 3.5 Sonnet, NotebookLM, Gemini 1.5 Advanced Ultra, ChatGPT o1-Pro, ChatGPT o1, GrokV2, DeepSeekV3, Gemini 2.0 Advanced with manual expert analysis. Jamovi software maintained methodological rigour through Cohen's Kappa coefficient calculations for concordance assessment and similarity measurement via Python using Jaccard index computations.
Results: Advanced A.I. models showed impressive congruence with reference standards; some even had perfect concordance (Jaccard index = 1.00). Gender-specific analyses demonstrated consistent performance across subgroups, allowing a nuanced understanding of psychosocial consequences. The grounded theory process developed the framework for the fragile circle of vulnerabilities that incorporated new insights into CL-related psychosocial complexity while establishing novel dimensions.
Conclusions: This study shows how A.I. can be incorporated in qualitative research methodology, particularly in complex psychosocial analysis. Consequently, the A.I. deep learning models proved to be highly efficient and accurate. These findings imply that the future directions for qualitative research methodology should focus on maintaining analytical rigour through the utilisation of technology using a combination of A.I. capabilities and human expertise following standardised future checklist of reporting full process transparency.
(© 2025. The Author(s).)

Declarations. Ethics approval and consent to participate: The ethical implications of A.I. utilisation in qualitative research were fully considered in this study, particularly concerning data confidentiality and methodological transparency. All analyses were conducted using ephemeral storage settings, ensuring no analysed data were archived. All AI-generated prompts and results were permanently discarded after hardware download to maintain data security. This study is a secondary analysis of anonymised qualitative data collected in a previous research study on the psychosocial impact of cutaneous leishmaniasis scars (Bennis et al., 2017) [20]. This study is a secondary analysis of anonymised qualitative data collected in a previous research study on the psychosocial impact of cutaneous leishmaniasis scars (Bennis et al., 2017) [20]. The original study was approved by the Ethical Committee of Biomedical Research in Rabat, Morocco (CERB). No additional ethics approval was required, as this study involved secondary data analysis. The dataset used was fully anonymised, and no new interactions with human participants occurred. The authors acknowledge that Grammarly.com for Microsoft Office Version 6.8.263 was used for language editing assistance, but it was not employed for generating original content. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.