Can ChatGPT write better scientific titles? A comparative evaluation of human-written and AI-generated titles

Study objective and design

This study aimed to evaluate the capacity of ChatGPT-4.0 to produce scientific article titles that are accurate, i.e., well aligned with the abstract content, appealing, and preferred by readers. We compared AI-generated titles with original human-written titles drawn from high-impact journals in general internal medicine. Our objective was to assess whether ChatGPT could match or surpass human authors in crafting titles that attract readers’ interest while accurately reflecting the abstract. To this end, we conducted a cross-sectional survey in which independent academic raters evaluated paired titles for each of fifty scientific abstracts. Each abstract was presented with two titles, one written by a human, the other generated by ChatGPT, in randomized order to avoid bias.

Journal and article selection

We first identified the ten general internal medicine journals with the highest impact factors in the 2023 Journal Citation Reports (JCR). To ensure consistency and relevance across journals, only those fulfilling all of the following criteria were eligible: they had to regularly publish original research and/or systematic reviews; they had to use structured abstracts for both types of articles; and they had to have been in continuous publication since at least January 2000. The year 2000 was deliberately chosen as the target publication period because it predates the availability of generative AI tools, eliminating any possibility that the original titles were AI-assisted. Based on these criteria, the following journals were selected: The Lancet (IF 98.4), The New England Journal of Medicine (IF 96.3), The BMJ (IF 93.7), JAMA (IF 63.5), Archives of Internal Medicine (IF 22.3), Annals of Internal Medicine (IF 19.6), CMAJ (IF 12.9), Journal of Travel Medicine (IF 9.1), Journal of Internal Medicine (IF 9.0), and Mayo Clinic Proceedings (IF 6.9).

From each eligible journal, we randomly selected five articles published between January 1 and December 31, 2000. These articles were either original research studies or systematic reviews. This sampling strategy resulted in a total of fifty abstracts, each with a corresponding human-written title. For each journal, the sampling frame consisted of all eligible articles published in 2000 that met the inclusion criteria. Articles were assigned numeric identifiers and selected using a computer-generated random number sequence.

AI-based title generation procedure

To generate alternative titles, we used the ChatGPT-4.0 model developed by OpenAI, which represents one of the most advanced publicly available LLMs at the time of the study. For each abstract, we initiated a new chat session with the model. This was done intentionally to eliminate contextual memory carryover and ensure that each title was generated independently of the others.

In each new session, the following standardized prompt was submitted: “Write a title for this scientific article based on the abstract below”. Immediately after entering the prompt, we pasted the full abstract of the selected article. The AI-generated title that resulted from this process was recorded verbatim and was not edited, reformulated, or shortened in any way by the researchers, except for standardizing capitalization: words were converted to lowercase when uppercase was not required (e.g., unless referring to names, countries, or other proper nouns). Capitalization was standardized across both human-written and AI-generated titles. This step was repeated for all fifty abstracts, yielding fifty unique AI-generated titles. The human-written and ChatGPT-generated titles are presented in the Supplementary Material.

Pairing and randomization of titles

Each abstract was thus associated with two titles: one written by the original human authors and the other generated by ChatGPT-4.0. For evaluation purposes, the two titles were assigned randomized positions as either “Title A” or “Title B” using a computer-generated random allocation procedure. This random order was intended to prevent raters from identifying which title had been written by a human and which by an AI, thereby minimizing bias during the evaluation process.

Questionnaire development and rating criteria

A structured evaluation questionnaire was developed to assess rater perceptions of the two titles accompanying each abstract. The survey presented all fifty abstracts, each introduced by two titles in randomized order (Title A and Title B), followed by the abstract itself. Each rater was asked to assess each title separately on two dimensions: first, how well the title represented the content of the abstract, and second, how much the title made them want to read the abstract or the full article.

These two dimensions (i.e., perceived alignment with the abstract content and appeal) were each rated using an ordinal scale ranging from 0 to 10. On this scale, a rating of 0 indicated an extremely negative judgment (e.g., not accurate or not appealing at all), a rating of 5 reflected a neutral or moderate assessment, and a rating of 10 indicated a highly positive evaluation (e.g., perfectly accurate or extremely appealing). Perceived alignment reflects how well the title was judged to match the content of the abstract, rather than verification of the factual or methodological correctness of the study itself.

After rating both titles on these two aspects, the raters were also asked to indicate which of the two titles they preferred overall, choosing either “Title A” or “Title B” for each abstract. The questionnaire and rating form are available in the Supplementary Material.

Rater recruitment and blinding

Twenty-one raters participated in the evaluation phase of the study. All were researchers who had authored at least one peer-reviewed academic publication. Eleven of these raters were recruited and contacted by one co-author (BN), and the remaining ten by another (PS), to ensure balanced recruitment. All participants provided informed consent in written electronic form (email agreement and completion of the questionnaire).

To avoid bias and maintain ecological validity, raters were not informed that one of the two titles had been generated by AI. They were simply told that the study aimed to examine how different formulations of article titles affect readers’ perceptions. No specific mention was made of ChatGPT or AI-based generation to preserve the authenticity of the evaluations.

Data collection timeline

The process of generating AI-based titles was completed in May 2025. The rating process, during which the twenty-one recruited raters completed the questionnaire, was conducted throughout June 2025. All ratings were submitted electronically and compiled in a central database for further statistical analysis.

Ethics and consent

This study did not require ethics committee approval under Swiss law, as no personal health data were collected (Human Research Act, HRA, art.2). All participants were adult researchers, informed about the study’s purpose (evaluating perceptions of different title formulations), voluntary participation, and anonymized handling of responses. To minimize bias, they were not told that one of the titles was AI-generated. Written informed consent was obtained via email agreement and completion of the questionnaire.

Statistical analysis

For each title, we calculated the mean (standard deviation, SD) and median (interquartile range, IQR) of rater scores for perceived alignment and appeal. To compare ratings between human-written and AI-generated titles, we used the Wilcoxon signed-rank test for paired data, as the ratings were ordinal and not normally distributed. For title preferences, we calculated the proportion of times each title was selected. Differences in preference proportions were tested using McNemar’s test, which is appropriate for paired categorical data.

In addition to these non-parametric tests, we conducted multilevel regression analyses to quantify effect sizes. Mixed-effects ordinal logistic regression models with random intercepts for both rater and article were used to compare perceived alignment and appeal ratings, yielding odds ratios (ORs). A mixed-effects logistic regression model with a random intercept for rater was used to assess the odds of preferring an AI-generated title over a human-written one.

To assess inter-rater agreement, we computed two measures separately for AI-generated and human-written titles: percent agreement and Gwet’s agreement coefficient (AC), using quadratic weights to account for the ordinal nature of the 0–10 rating scale.^32–34 Agreement levels were computed across the 21 raters and stratified by rating dimension (perceived alignment and appeal). The weighted analysis assigns partial credit for near agreement, making it more appropriate for ordinal data. We interpreted Gwet’s AC using the classification proposed by Landis and Koch (1977): values <0.00 indicate poor agreement, 0.00–0.20 slight, 0.21–0.40 fair, 0.41–0.60 moderate, 0.61–0.80 substantial, and 0.81–1.00 almost perfect agreement.³⁵

We did not perform subgroup analyses based on rater characteristics, as the limited number of raters (N = 21) would not have allowed for statistically meaningful comparisons. All analyses were conducted using Stata version 15.1 (StataCorp, College Station, TX, USA). A two-sided p-value < 0.05 was considered statistically significant.

Rater characteristics

The main characteristics of the 21 raters who participated in the study are presented in Table 1. Twelve were women and nine were men. Twelve were under 40 years of age, eight were between 40 and 60 years, and one was over 60 years old. The raters were primarily from China (n = 11) and Switzerland (n = 8), with one rater each from the United States and France. They had diverse academic and professional backgrounds. Among them, five specialized in library and information science, and seven in general internal medicine.

Perceived accuracy and appeal ratings

For consistency with the original rating instrument, the term “perceived accuracy” is retained in this section. In the context of this study, this term refers to raters’ perceived alignment between the title and the abstract content, rather than verification of factual or methodological correctness.

Table 2 presents the median, IQR, and minimum–maximum values of rater scores for perceived accuracy and appeal, stratified by title type (AI-generated vs. human-written) and by individual rater. Figures 1 and 2 display these distributions using boxplots, one per rater, for perceived accuracy and appeal, respectively. Overall, AI-generated titles received more favorable ratings. For perceived accuracy, 18 raters rated AI-generated titles higher than human-written titles, three gave equal ratings, and none rated AI-generated titles lower. For appeal, 12 raters rated AI-generated titles higher, five gave equal ratings, and four preferred human-written titles.

Figure 1. Boxplots showing perceived accuracy ratings for AI-generated and human-written titles for each of the 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals.

Figure 2. Boxplots showing appeal ratings for AI-generated and human-written titles for each of the 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals.

As summarized in Table 3 and visualized in Figure 3, AI-generated titles received significantly higher scores. For perceived accuracy, the mean score was 7.9 for AI-generated titles compared to 6.7 for human-written titles, with a median of 8 versus 7 (p-value < 0.001). For appeal, the mean score was 7.1 for AI-generated titles versus 6.7 for human-written titles, with a median of 7 for both (p-value < 0.001). In multilevel models, AI-generated titles had higher odds of receiving higher ratings for perceived accuracy (OR 4.4, 95% CI 3.7–5.2; p-value < 0.001) and appeal (OR 1.7, 95% CI 1.5–2.0; p-value < 0.001) than human-written titles.

Figure 3. Boxplots showing perceived accuracy and appeal ratings for AI-generated and human-written titles by 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals.

Title preferences

Overall preferences also favored AI-generated titles. As shown in Figure 4, 16 out of 21 raters preferred AI-generated titles, while five preferred human-written ones. Among the 1,049 pairwise preference judgments (out of a possible 1,050; one missing value), 61.8% favored the AI-generated title and 38.2% favored the human-written title (p-value < 0.001; Table 3). The odds of preferring an AI-generated title were 1.7 times higher than those of preferring a human-written title (p-value = 0.001).

Figure 4. Proportion of AI-generated title preferences for each of the 21 raters, based on 50 scientific titles from 10 high-impact general internal medicine journals.

Inter-rater agreement

Table 4 presents inter-rater agreement measures by title type. Percent agreement ranged from 88.9% to 92.5%, while Gwet’s ACs, calculated using quadratic weights for ordinal scales, ranged from 0.54 to 0.70. These values indicate moderate to substantial agreement according to the benchmark scale proposed by Landis and Koch (1977),³⁵ suggesting consistent tendencies across raters while also highlighting that judgments of title quality remain partly subjective.

Summary of key findings

This study evaluated how 21 raters assessed the perceived alignment between title and abstract, appeal, and overall preference of 50 scientific titles, comparing AI-generated and human-written versions. AI-generated titles received significantly higher ratings for both perceived alignment and appeal, with most raters favoring them over human-written alternatives. In total, 61.8% of preference judgments were in favor of AI-generated titles, and inter-rater agreement ranged from moderate to substantial.

Comparison with literature

Our findings are consistent with a growing body of literature suggesting that LLMs such as GPT-4.0 can generate high-quality scientific text that is often indistinguishable from human-written content.^20,36–42 Our results go beyond prior work by focusing specifically on titles, a concise yet crucial form of scientific communication. Unlike abstracts or full texts, titles must strike a balance between informativeness, clarity, and appeal in a highly constrained format. While some recent studies have explored AI-generated titles, they have either emphasized stylistic aspects such as humor and novelty in technical fields or evaluated output using only automated similarity metrics, without considering how human readers perceive title quality.^30,31 The fact that AI-generated titles scored higher on both perceived alignment with the abstract content and appeal challenges assumptions that LLMs lack the nuance or domain expertise to outperform human authors in such a delicate task. This suggests that LLMs may be particularly well suited for short-form scientific writing, where lexical clarity and stylistic optimization matter more than in-depth reasoning.

Importantly, our study focused exclusively on articles from high-impact general internal medicine journals, where title quality is expected to be particularly high due to rigorous editorial and peer-review processes. If AI-generated titles can outperform those published in such venues, the gap may be even greater for titles in lower-tier journals, where writing quality is more variable. Future research should investigate whether similar results hold across different fields, disciplines, and levels of journal prestige.

Collectively, our study complements and extends previous research by offering a detailed, comparative analysis of AI vs. human performance in scientific titling, a topic that has received relatively little empirical attention but has major implications for academic publishing practices. However, our findings should be interpreted primarily within contexts similar to those examined in our study (e.g., biomedical research evaluated by non-specialist academic readers).

Implications for practice and research

From a practical standpoint, the finding that AI-generated titles are rated more highly than human-written ones suggests that LLMs could be reliably used to assist researchers in generating or refining article titles. Given that titles play a key role in shaping reader perceptions, citation rates, and online discoverability, tools that enhance title quality could have a direct impact on dissemination and academic impact. In particular, researchers with limited writing experience or for whom English is not a first language might benefit from LLM-based titling tools to improve clarity and reader engagement.

The observed preferences imply that AI-generated suggestions may outperform human intuition in specific aspects of scientific writing, such as title generation. This raises the possibility of integrating AI assistance more formally into journal workflows, for example through automated title suggestions during the submission process or editorial review. While this would require careful oversight, our data indicate that such tools would not compromise, and may even enhance, perceived quality.

However, it is important to note that higher appeal or preference does not necessarily imply greater epistemic rigor. Titles optimized for engagement may emphasize clarity or assertiveness while potentially downplaying uncertainty or methodological nuance. Moreover, the integration of AI into scholarly communication also raises critical ethical questions.^29,43–46 These concerns echo ongoing debates about the role of LLMs in scientific authorship and the boundaries of acceptable assistance. Our findings underline the importance of maintaining transparent authorship practices and labeling AI contributions in scientific writing, even if such tools are only used to generate the title of the article. Beyond ethical issues, the widespread application of AI in generating titles may lead to homogenization in academic writing, resulting in titles that tend to fall within a narrow stylistic range and suppress the diversity, creativity, and uniqueness of the disciplines. These considerations relate to ongoing discussions in scholarly publishing regarding whether AI-assisted writing should be regarded as authorship, editorial assistance, or technical support, and how journals might operationalize transparent disclosure of AI use.

From a research perspective, our study opens several avenues for further investigation. One important direction is to test the generalizability of these findings across disciplines, languages, and types of scientific content. It is possible that preferences for AI-generated titles vary depending on disciplinary norms or journal styles. In addition, future work could examine how title preferences correlate with actual article impact, such as downloads, citations, or Altmetric scores, to determine whether rater judgments align with broader readership behavior. Another key area for future research is to understand the mechanisms behind rater preferences. For example, are AI-generated titles preferred because of greater lexical simplicity, more direct structure, or the avoidance of technical jargon? Applying NLP tools to analyze linguistic features could shed light on what drives these preferences and help refine AI title generation even further. Lastly, as LLMs continue to evolve, longitudinal studies will be needed to assess how perceptions of AI-generated text change over time and whether improvements in model quality lead to higher standards or greater acceptance.

Limitations

This study has several limitations that should be acknowledged. First, although the use of articles from the year 2000 ensured that original titles were free from AI influence, it also introduces a potential temporal bias. Scientific writing conventions and stylistic preferences may have evolved over the past two decades, and what was considered an effective title in 2000 may differ from current standards. In other words, because AI-generated titles are produced by models trained largely on contemporary scientific language, differences may partly reflect shifts in stylistic conventions and reader expectations over time. Second, although we recruited raters with relevant academic experience, the sample size (N = 21) remains relatively small, and their subjective preferences may not fully represent broader readership or editorial perspectives. Third, while the zero-shot setting of ChatGPT-4.0 reflects real-world usage by non-expert users, it may not capture the full potential of LLMs when used with prompt optimization or human-in-the-loop refinement. Additionally, the evaluation focused on only two dimensions (i.e., perceived accuracy and appeal) along with an overall preference rating. Other important aspects of scientific titles, such as precision, cautiousness of claims, clarity, informativeness, tone, and appropriateness for indexing or search engine optimization, were not explicitly assessed. In particular, titles rated as more appealing may not necessarily reflect more rigorous or conservative scientific framing, and this potential trade-off warrants further investigation. Lastly, the study did not include domain experts for each article’s specific topic area, which may have influenced the ability of raters to judge how well a title reflected the article’s nuanced content.

Future research could expand upon this work by including more diverse raters, evaluating newer articles, testing various prompting strategies, and incorporating additional dimensions of title quality. Despite these limitations, our findings provide valuable insights into the potential of LLMs to assist in academic title generation and highlight the subjective nature of title preferences.