How ChatGPT writes scientific titles in medical research: structural and content differences compared to human authors.

Abstract

Objective: Scientific article titles play a central role in shaping research visibility, interpretation, and discoverability. With the rise of large language models like ChatGPT, there is growing interest in using AI tools to support title generation, yet little is known about how AI-generated titles differ from those written by human authors. This study compared titles written by original authors to those generated by ChatGPT-4.0 from the same abstracts, focusing on structural and content-level differences.

Methods: Fifty research articles published in 2000, before the advent of generative AI, were randomly selected from ten high-impact general internal medicine journals. For each, the structured abstract was submitted to ChatGPT-4.0 using a standardized prompt to generate a title. Human-written and AI-generated titles were then compared using quantitative measures (word and character counts, punctuation marks) and descriptive content analysis (study design, population descriptors, outcome emphasis, public health or clinical framing, temporal context). Paired statistical tests were applied to assess differences.

Results: ChatGPT-generated titles were significantly longer than human-written titles (median 16 vs. 12.5 words, p-value<0.001) and included more characters and punctuation marks (colons in 100% vs. 30%; p-value<0.001). AI titles more often specified or clarified study design, detailed populations, emphasized outcomes, framed findings in public health or clinical terms, and incorporated temporal context.

Conclusion: ChatGPT-4.0 produces more explicit and structured titles than human authors, emphasizing methodological clarity and content completeness. These findings raise important questions about norms in scientific communication and the need for further research and ethical guidance on AI-assisted writing.