Artificial or Authentic? A Comparative Study of Article Titles Generated by Humans and AI in Physical Medicine and Rehabilitation
Öz
Abstract
Background:
This study examines expert preferences and perceptions regarding whether article titles in the field of physical medicine and rehabilitation are generated by humans or artificial intelligence (AI). It aims to evaluate the selected titles in terms of scientific accuracy, comprehensibility, originality, and attractiveness.
Methods:
Thirty-two article titles published in a Q1 journal indexed in the Web of Science database were selected. For each, an alternative AI-generated title was created using the ChatGPT-4o model. A survey was administered to 15 academic participants with an H-index ≥3. For each title pair, participants indicated their preference and the reason for their choice. The data were analyzed using SPSS v22 software.
Results:
A total of 480 title choices were recorded, with 58.1% favoring AI-generated titles. This difference was statistically significant (p=0.008). Overall, 93.3% of participants preferred AI titles more frequently. The most cited reason for preference was “comprehensibility” (47.3%). No statistically significant difference was found based on demographic variables.
Conclusions:
AI-generated titles were found to be strong in terms of clarity and attractiveness. However, “scientific accuracy” was cited more frequently in favor of original titles. This indicates that while AI is effective in generating appealing titles, human contribution remains necessary for ensuring scientific rigor. Hybrid models combining AI and human input may offer a balanced approach to scientific title generation.
Anahtar Kelimeler
Artificial Intelligence, Academic Writing, Physical Medicine and Rehabilitation
Etik Beyan
Since the study did not involve human or animal subjects, the data were obtained from the Web of Science database. Therefore, ethical committee approval was not required.
Teşekkür
We sincerely thank all the professors who generously dedicated their valuable time to complete the survey for our study.
Kaynakça
- 1. Jamali HR, Nikzad M. Article title type and its relation with the number of downloads and citations. Scientometrics. 2011;88(2):653-61.
- 2. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113-31.
- 3. Sardanelli F, Castiglioni I, Colarieti A, Schiaffino S, Di Leo G. Artificial intelligence (AI) in biomedical research: discussion on authors' declaration of AI in their articles' title. Eur Radiol Exp. 2023;7(1):2.
- 4. Marlow R, Wood D. Ghost in the machine or monkey with a typewriter-generating titles for Christmas research articles in The BMJ using artificial intelligence: observational study. BMJ. 2021;375:e067732.
- 5. Hamel C, Hersi M, Kelly SE, Tricco AC, Straus S, Wells G, et al. Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses. BMC Med Res Methodol. 2021;21(1):285.
- 6. Chen L, Chen P, Lin Z. Artificial intelligence in education: A review. IEEE Access. 2020;8:75264-78.
- 7. Sayar B. Tıp alanında yapay zekânın kullanımı: araştırma makalesi. Acta Med Ruha. 2023;1(1):27-33.
- 8. Yıldıran G, Demirtaş S, Akdeniz H, Tosun Z. Which is superior in creating scientific article titles in plastic surgery: human intelligence or artificial intelligence? Genel Tip Derg. 2025;35(2):222-5.
- 9. Putra JWG, Khodra ML. Automatic title generation in scientific articles for authorship assistance: a summarization approach. J ICT Res Appl. 2017;11(3):253-67.
- 10. Huang J, Tan M. The role of ChatGPT in scientific communication: writing better scientific review articles. Am J Cancer Res. 2023;13(4):1148.