Can Artificial Intelligence Replicate Human Qualitative Analysis?

INTRODUCTION: Qualitative research is essential in surgical education for exploring complex social phenomena. However, thematic analysis is time-intensive, requires methodological expertise, and is inherently subject to interpretive bias. Artificial intelligence (AI) has increasingly been explored as a tool to support qualitative analysis, though its role in interpreting complex, abstract concepts remains unclear. The aim of this study was to evaluate whether generative AI can perform qualitative thematic analysis of abstract concepts by comparing AI-generated themes with human-derived themes. METHODS: We conducted a secondary comparative analysis using transcripts from two previously completed thematic analyses examining how general surgery residency applicants define wellness and engagement. Human-derived themes were generated using an inductive immersion-crystallization approach. The same deidentified transcripts were then analyzed by ChatGPT version 4.0 using a single prompt to generate four themes per dataset without iterative refinement. Human- and AI-generated themes were compared descriptively for conceptual overlap, alignment and interpretive consistency. RESULTS: A total of 117 applicants were interviewed. Visual mapping demonstrated substantial conceptual overlap between human- and AI-generated themes for both wellness and engagement, with no unique or contradictory themes identified by AI. Human analysts tended to generate discrete themes separating individual- and group-level constructs. In contrast, AI-generated themes integrated these constructs into broader, relational constructs while preserving core thematic content. CONCLUSIONS: Generative AI demonstrated meaningful alignment with human thematic analysis. When used as a complementary analytic tool with appropriate human oversight, AI may enhance efficiency and accessibility of qualitative methods in surgical education research without replacing interpretive judgment.