Scientific Writing in the Era of Large Language Models: A Computational Analysis of AI- Versus Human-Created Content
Large language models generate scientific content with measurable differences versus human-written text but represent features that are not consistently identifiable even by human experts and require complex AI detection tools. Given the challenges that experts face in distinguishing AI from human content, technology-assisted tools are essential wherever human provenance is essential to safeguard the integrity of scientific communication.
Authors
- Rohan Khera (ORCID: https://orcid.org/0000-0001-9467-6199)
- Gisele Sampaio Silva (ORCID: https://orcid.org/0000-0002-3247-3123)
- Aline F Pedroso (ORCID: https://orcid.org/0000-0002-1876-8304)
- Vipina K. Keloth (ORCID: https://orcid.org/0000-0001-6919-1122)
- Hua Xu (ORCID: https://orcid.org/0000-0002-5274-4672)
- Lee H. Schwamm (ORCID: https://orcid.org/0000-0003-0592-9145)
Institutions
- Yale New Haven Hospital (US)
- Hospital Israelita Albert Einstein (BR)
- Yale University (US)
- Yale New Haven Health System (US)
Publication Details
- Journal
- Stroke
- Published
- 2025-08-15
- DOI
- https://doi.org/10.1161/strokeaha.125.051913
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65