Zero-Shot PI-RADS Version 2.1 Scoring with ChatGPT-4 Turbo and Llama 3: Diagnostic Performance and Agreement with Abdominal Radiologists
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) like ChatGPT-4 Turbo and Llama 3 show high agreement in assigning Prostate Imaging Reporting and Data System (PI-RADS) scores for prostate MRI reports, aiding oncologists.
Area Of Science
- Radiology and Medical Imaging
- Artificial Intelligence in Healthcare
- Oncology and Prostate Cancer Diagnostics
Background
- Prostate MRI interpretation relies on standardized scoring systems like the Prostate Imaging Reporting and Data System (PI-RADS).
- Accurate PI-RADS scoring is crucial for diagnosing prostate cancer and guiding treatment decisions.
- Evaluating the role of emerging AI technologies, specifically large language models (LLMs), in clinical reporting is essential.
Purpose Of The Study
- To assess the diagnostic performance and inter-rater agreement of ChatGPT-4 Turbo and Llama 3 in assigning PI-RADS scores to prostate MRI reports.
- To compare the performance of these LLMs against experienced abdominal radiologists.
- To determine the potential of LLMs to enhance consistency and accuracy in prostate MRI reporting.
Main Methods
- A retrospective analysis of 500 structured prostate MRI reports was conducted, with original PI-RADS scores anonymized.
- Two LLMs (ChatGPT-4 Turbo and Llama 3) were employed using a standardized prompt to extract PI-RADS version 2.1 scores.
- Two abdominal radiologists independently assigned PI-RADS scores, with discrepancies resolved by a third radiologist; prostate biopsy results served as the reference standard.
Main Results
- Both LLMs demonstrated high agreement with radiologists (94.7%-95.7% agreement, κ = 0.89-0.91).
- ChatGPT-4 Turbo showed a trend towards higher PI-RADS scores compared to radiologists (P < .005).
- Area under the receiver operating characteristic curves (AUCs) for predicting prostate cancer were comparable: 0.79 for ChatGPT-4 Turbo and original reports, 0.78 for radiologists and Llama 3.
Conclusions
- Large language models exhibit strong agreement with expert radiologists in PI-RADS scoring for prostate MRI.
- LLMs show potential to improve the accuracy and consistency of prostate MRI reporting, supporting oncological diagnosis.
- Further integration of LLMs could streamline radiology workflows and enhance diagnostic capabilities in prostate cancer detection.
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