Using ChatGPT-4 for Lay Summarization in Prostate Cancer Research to Advance Patient-Centered Communication: Large-Scale Generative AI Performance Evaluation

Abstract

Background: The increasing volume and complexity of biomedical literature pose challenges for making scientific knowledge accessible to lay audiences. Lay summaries, now widely encouraged or required by journals, aim to bridge this gap by promoting health literacy, patient engagement, and public trust. However, many are written by scientists without formal training in plain-language communication, often resulting in limited clarity, readability, and consistency. Generative large language models such as ChatGPT-4 offer a scalable opportunity to support lay summary creation, though their effectiveness within specific clinical domains has not been systematically evaluated at scale. Objective: This study aimed to assess ChatGPT-4’s performance in generating lay summaries for prostate cancer studies. A secondary objective was to evaluate how prompt design influences summary quality, aiming to provide practical guidance for the use of generative artificial intelligence (AI) in scientific publishing. Methods: A total of 204 consecutive articles on prostate cancer were extracted from a high-ranking oncology journal mandating lay summaries. Each abstract was processed with ChatGPT-4 using 2 prompts: a simple prompt based on the journal’s guidelines and an extended prompt refined to improve readability. AI-generated and original summaries were evaluated using 3 criteria: readability (Flesch-Kincaid Reading Ease [FKRE]), factual accuracy (5-point Likert scale, blinded rating by 2 clinical experts), and compliance with word count instructions (120‐150 words). Summaries were classified as high-quality as a composite outcome if they met all 3 benchmarks: FKRE >30, accuracy ≥4 from both raters, and word count within range. Statistical comparisons used Wilcoxon signed-rank and paired 2-tailed t tests (P