Improving Readability and Automating Content Analysis of Plastic Surgery Webpages With ChatGPT.

INTRODUCTION

The quality and readability of online health information are sometimes suboptimal, reducing their usefulness to patients. Manual evaluation of online medical information is time-consuming and error-prone. This study automates content analysis and readability improvement of private-practice plastic surgery webpages using ChatGPT.

METHODS

The first 70 Google search results of "breast implant size factors" and "breast implant size decision" were screened. ChatGPT 3.5 and 4.0 were utilized with two prompts (1: general, 2: specific) to automate content analysis and rewrite webpages with improved readability. ChatGPT content analysis outputs were classified as hallucination (false positive), accurate (true positive or true negative), or omission (false negative) using human-rated scores as a benchmark. Six readability metric scores of original and revised webpage texts were compared.

RESULTS

Seventy-five webpages were included. Significant improvements were achieved from baseline in six readability metric scores using a specific-instruction prompt with ChatGPT 3.5 (all P ≤ 0.05). No further improvements in readability scores were achieved with ChatGPT 4.0. Rates of hallucination, accuracy, and omission in ChatGPT content scoring varied widely between decision-making factors. Compared to ChatGPT 3.5, average accuracy rates increased while omission rates decreased with ChatGPT 4.0 content analysis output.

CONCLUSIONS

ChatGPT offers an innovative approach to enhancing the quality of online medical information and expanding the capabilities of plastic surgery research and practice. Automation of content analysis is limited by ChatGPT 3.5's high omission rates and ChatGPT 4.0's high hallucination rates. Our results also underscore the importance of iterative prompt design to optimize ChatGPT performance in research tasks.