GPT-4o, with prompt engineering, selected optimal abdominal/pelvic CT protocols more accurately than radiologists without increasing inappropriate selections.
Key Details
- 1Study evaluated 1,448 abdominal and pelvic CT exams between Jan-June 2024.
- 2GPT-4o with detailed prompting selected optimal protocols 96.2% of the time, compared to 88.3% for radiologists (p<0.001).
- 3Rates of inappropriate protocols were similarly low: 1.3% (GPT-4o) vs. 2.4% (radiologists), not statistically significant (p=0.21).
- 4Fine-tuning GPT-4o offered no performance increase over meticulous prompting (both 96.2%).
- 5Performance in protocol matching was consistent across training levels (radiologist, fellow, resident).
Why It Matters
Automating protocol assignment with LLMs like GPT-4o could streamline imaging workflow, freeing radiologists for more complex tasks and reducing the risk of workflow interruptions and diagnostic errors. The approach may allow for adaptable, institution-specific automation without intensive retraining.
Source
AuntMinnie
Related News
•AuntMinnie
AI Model Uses Ultrasound to Assess Fetal Lung Maturity
Researchers demonstrated an AI model's strong accuracy in measuring fetal lung maturity from ultrasound images.
•AuntMinnie
AI Model Predicts Dosimetry for Lu-177 PSMA Therapy Using PET/CT
A machine learning PET/CT model shows promise for predicting radiation dose prior to Lu-177 PSMA therapy in prostate cancer patients.
•AuntMinnie
AI Advances in Ultrasound Highlighted at AIUM 2026 Keynote
AI is increasingly enhancing ultrasound imaging, clinical workflows, and education, though challenges in trust and implementation remain.