ArticleBMC medical informatics and decision making2025
Advancing AI-driven thematic analysis in qualitative research: a comparative study of nine generative models on Cutaneous Leishmaniasis data.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Digital Technologies for Community Consultation and Public Disclosure in Exception From Informed Consent Trials.Journal of the American College of Emergency Physicians open · 2026Article
- Stakeholder perspectives on implementing ED time series forecasting: a qualitative interview study.BMC emergency medicine · 2026Article
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
- HRV-Based Multimodal Physiological Signal Monitoring Using Wearable Biosensors in Human-Computer Interaction: Cognitive Load in Real-Time Strategy Games.Sensors (Basel, Switzerland) · 2026Article
- Large language models for thematic analysis in healthcare research: A blinded mixed-methods comparison with human analysts.PLOS digital health · 2026Article
- Spiritual Intelligence: A Scoping Review with Concept Analysis on the Key to Spiritual Care.Journal of Intelligence · 2026Review
- Self-directed speech and attention deficit hyperactive disorder-like behaviours.British journal of psychology (London, England : 1953) · 2026Article
- Impact of Community-Oriented Medical Education on Medical Students' Perceptions of Community Health Care: Qualitative Study.JMIR medical education · 2026Article
- Medical challenges and unmet needs of individuals with 22q11.2 deletion syndrome as perceived by caregivers: A thematic analysis and natural language processing-based thematic extraction.PLOS mental health · 2026Article
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- Extension of the Consolidated Criteria for Reporting Qualitative Research Guideline to Large Language Models (COREQ+LLM): Protocol for a Multiphase Study.JMIR research protocols · 2025Article
- AI tools accelerating One Health advancement: insights from the Global Public Health Week 2025 Webinar.Science in One Health · 2025Review
- Navigating the meta-crisis of generativity: adapting qualitative research quality criteria in the era of generative AI.Frontiers in research metrics and analytics · 2025Article
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- Planetary health education in practice: public health, climate change, and transdisciplinary learning at University of New England.Frontiers in public health · 2025Article
- Multimorbidity and AI-enabled health and social care: A methodological illustration of integrating large language models into qualitative analytic workflows.Journal of multimorbidity and comorbidityArticle
- Article
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Abstract
backgroundAs 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.
methodsThe 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.
resultsAdvanced 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.
conclusionsThis 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.
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