Stanford and Mayo Clinic Arizona researchers demonstrated that LLMs like GPT-4 can categorize critical findings in radiology reports using few-shot prompting.
Key Details
- 1GPT-4 and Mistral-7B LLMs tested for classifying critical findings in radiology reports from ICU patients.
- 2252 MIMIC-III reports (mixed modalities: 56% CT, ~30% x-ray, 9% MRI) and 180 external chest x-ray reports evaluated.
- 3LLMs categorized findings as true, known/expected, or equivocal critical findings.
- 4GPT-4 achieved 90.1% precision and 86.9% recall for true critical findings in internal test set; 82.6% precision and 98.3% recall in external test set.
- 5Mistral-7B showed lower precision (75.6%) but comparable recall (77.4%-93.1%).
- 6Study highlights few-shot prompting as an efficient strategy; real-world deployment requires further refinement.
Why It Matters
This research suggests that off-the-shelf LLMs could automate detection of urgent findings in radiology workflow with minimal manual annotation, potentially easing communication bottlenecks and improving patient safety. Further development and EHR integration will be needed for clinical implementation.
Source
AuntMinnie
Related News
•AuntMinnie
LLMs Demonstrate Strong Potential in Interventional Radiology Patient Education
DeepSeek-V3 and ChatGPT-4o excelled in accurately answering patient questions about interventional radiology procedures, suggesting LLMs' growing role in clinical communication.
•Radiology Business
Women's Uncertainty About AI in Breast Imaging May Limit Acceptance
Many women remain unclear about the role of AI in breast imaging, creating hesitation toward its adoption.
•AuntMinnie
Stanford Team Introduces Real-Time AI Safety Monitoring for Radiology
Stanford researchers introduced an ensemble monitoring model to provide real-time confidence assessments for FDA-cleared radiology AI tools.