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Comparing GPT-4 and Human Researchers in Health Care Data Analysis: Qualitative Description Study.

Kevin Danis Li^1,2, Adrian M Fernandez¹, Rachel Schwartz^3,4

¹Department of Urology, University of California San Francisco, San Francisco, CA, United States.

Journal of Medical Internet Research

|August 21, 2024

Summary

This summary is machine-generated.

Large language models like GPT-4 can identify key themes in qualitative health research, showing moderate agreement with human analysis. While humans offer richer subthemes, AI provides consistent coding, suggesting its use as a complementary tool.

Keywords:

ChatGPT artificial intelligence buried penis content analysis large language models qualitative analysis qualitative description qualitative interviews urology

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Area of Science:

Medical research
Artificial intelligence in healthcare
Qualitative data analysis

Background:

Large language models (LLMs) like GPT-4 offer advanced capabilities for healthcare and qualitative research.
Traditional qualitative methods are resource-intensive and require specialized expertise.
The performance of LLMs in qualitative analysis compared to human researchers is not well-understood.

Purpose of the Study:

To evaluate the effectiveness of GPT-4 against human researchers in the qualitative analysis of patient interviews.
To compare theme and subtheme identification between AI and human analysis in a specific patient population.

Main Methods:

Qualitative analysis of semistructured interviews with 20 patients with adult-acquired buried penis (AABP).
Human analysis involved a three-stage process: observation, coding, and consensus discussion.
AI analysis with GPT-4 included a naive phase for theme identification and a comparison phase against human-identified themes.

Main Results:

Both human and GPT-4 analyses identified key themes such as "urinary issues," "sexual issues," and "mental health issues."
Human analysis revealed a broader range of subthemes and uniquely identified "contributing factors."
Moderate agreement (κ=0.401) was observed between human and GPT-4 coding; GPT-4 demonstrated reliable coding for several themes.

Conclusions:

LLMs like GPT-4 can effectively identify key themes in qualitative healthcare data, demonstrating moderate agreement with human analysis.
While human analysis offers greater thematic diversity, AI's consistency supports its role as a complementary research tool.
Future research should explore AI-driven qualitative analysis, addressing limitations like token constraints to enhance breadth and depth.