Collaborative Framework Integrating Human and Artificial Intelligence for Thematic Analysis in Digital Health Interviews

Overview

This study evaluates the integration of human and artificial intelligence in thematic analysis of digital health interviews.

Background

Qualitative methods, particularly thematic analysis, are essential in health research for understanding patient experiences and improving interventions. The integration of AI, especially LLMs, into qualitative analysis presents new opportunities and challenges.

Data Highlights

No numerical or trial data provided in the source material.

Key Findings

• Thematic analysis is a flexible method for identifying patterns in qualitative data.
• LLMs can assist in coding and theme generation but require human oversight for validation.
• Studies show mixed results regarding the effectiveness of LLMs in qualitative analysis.
• Transparency and methodological rigor are essential when integrating AI in qualitative workflows.
• Human judgment remains critical in interpreting AI-generated themes.

Clinical Implications

Healthcare professionals should be aware of the strengths and limitations of AI tools in qualitative research.

Conclusion

The integration of AI in thematic analysis necessitates careful implementation to uphold research quality.

Related Resources & Content

1. Journal of Medical Internet Research (JMIR), 2026 -- Artificial Intelligence in Patient-Centered Care and Macro-, Meso-, and Micro-Level Determinants of Rehumanization and Dehumanization: Qualitative Interview Study
2. npj Digital Medicine, 2026 -- A qualitative interview study investigating patient, health professional, and developer perspectives on real-world implementation of patient-centered AI systems
3. Journal of Medical Internet Research (JMIR), 2026 -- Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal
4. Frontiers in Digital Health, 2026 -- Perspectives on healthcare artificial intelligence policy from health equity professionals: findings from an interview study
5. The use and methodological reporting of large language models in qualitative research: a scoping review | BMC Medical Research Methodology | Springer Nature Link
6. Artificial intelligence and evidence-informed policy: emerging challenges and opportunities: discussion paper
7. A fine-tuned large language model chatbot for multi-scenario radiology cancer care: randomized controlled trial on interaction optimization, emotional support, and provider burnout reduction | Journal of Translational Medicine | Springer Nature Link
8. The use and methodological reporting of large language models in qualitative research: a scoping review | BMC Medical Research Methodology | Springer Nature Link
9. Artificial intelligence and evidence-informed policy: emerging challenges and opportunities: discussion paper
10. A fine-tuned large language model chatbot for multi-scenario radiology cancer care: randomized controlled trial on interaction optimization, emotional support, and provider burnout reduction | Journal of Translational Medicine | Springer Nature Link