Using Artificial Intelligence in Qualitative Lupus Pregnancy Research: The Potential of Large Language Models and Prompting Strategies.

OBJECTIVE: Our objective was to evaluate the ability of different large language models (LLMs) to translate and analyze qualitative interviews describing pregnancy experiences among women with systemic lupus erythematosus (SLE). An additional objective was to compare themes identified by LLMs with those identified by human researchers. METHODS: We analyzed 26 semi-structured interviews originally conducted in Portuguese by Rodrigues et al. among women with SLE exploring their perinatal care experiences. Three prompting strategies (zero-shot, few-shot, and chain-of-thought) were used on the transcripts across four state-of-the-art LLMs (GPT-4o, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek R) to generate key themes. Model outputs were evaluated using an LLM-as-a-judge framework assessing thematic coverage, thematic unity, and semantic similarity. RESULTS: Across models and prompting strategies, LLMs generated similar themes that closely aligned with those identified by human researchers. LLM-as-a-judge evaluations demonstrated high thematic unity and semantic similarity. Claude Sonnet 4.5 showed the most consistent performance. Few-shot prompting consistently outperformed zero-shot and chain-of-thought approaches. CONCLUSION: LLMs show promise as supportive tools for analyzing qualitative research by preserving thematic coherence and capturing the complexity of pregnancy experiences in women with SLE. Although zero-shot prompting is widely used, it produced lower thematic results in our study, whereas few-shot prompting achieved the most comprehensive and coherent theme extraction. LLM-assisted qualitative analysis may complement human interpretation and has the potential to support the inclusion of non-English language data and diverse patient perspectives in lupus research.