Radiology research abstracts show a marked rise in LLM-assisted editing since the release of ChatGPT.
Key Details
- 1A study analyzed 32,335 English-language medical research abstracts (2020β2025), including 23,227 journal articles and 9,108 preprints across radiology, surgery, and internal medicine.
- 2For radiology journal articles, LLM-modified text increased from 0.5% pre-ChatGPT to 3% post-ChatGPT; for preprints, it rose from 1.4% to 7%.
- 3βAlphaβ values measuring LLM influence grew from 1.1% (Q1 2023) to 6.5% (Q2 2025) in radiology articles and showed increases in Asia, Europe, and North America.
- 4Surgery and internal medicine also saw post-ChatGPT increases, though radiology preprints saw the sharpest rise.
- 5Authors caution that LLM use in writing isn't necessarily misconduct, as many journals have guidance on responsible AI use in scientific writing.
Why It Matters
Growing adoption of LLMs for drafting and editing radiology abstracts has implications for publication norms, author accountability, and transparency in radiological research. Understanding these trends can help journals and researchers align practices with evolving technology.

Source
AuntMinnie
Related News

β’AuntMinnie
AI Models Reveal Racial Disparities in Breast Cancer Patterns
Machine learning models reveal significant racial disparities and key predictors in breast cancer incidence across diverse groups.

β’AuntMinnie
AI Algorithm Streamlines and Standardizes Shoulder Ultrasound Acquisition
A multitask AI system demonstrated high accuracy in standardizing and guiding shoulder musculoskeletal ultrasound imaging.

β’Radiology Business
Deepfake X-rays Fool Radiologists and AI, Raising Security Concerns
Both radiologists and AI models struggle to differentiate between authentic and AI-generated ('deepfake') radiographic images, raising major security and clinical concerns.