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 (2020–2025), 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%.
- 3‘Alpha’ 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
•AuntMinnie
Study: Computer Vision Models Best LLMs in Chest CT Breast Abnormality Detection
Computer vision models (CVMs) surpass large language models (LLMs) in accurately labeling incidental breast abnormalities on chest CT scans.
•AuntMinnie
Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.
•Radiology Business
Radiology AI Devices at Elevated Risk for FDA Recalls, Study Finds
Radiology AI devices are more likely to face FDA recalls, largely due to deviations from intended use and incomplete clinical data.