Large language models demonstrate promising performance in automating PI-RADS classification from structured prostate MRI reports, with some limitations in intermediate-risk lesions.
Key Details
- 1Study included 146 structured prostate MRI reports from October 2023 to October 2024.
- 2Four LLMs compared: GPT-4o, GPT-o1, Google Gemini 1.5 Pro, Google Gemini 2.0 Experimental Advanced.
- 3Radiologist consensus used as ground truth; Cohen's kappa measured agreement.
- 4GPT-o1 achieved the highest agreement (kappa = 0.87) and perfect F1 score (1.00) for high-risk PI-RADS category.
- 5All LLMs struggled with PI-RADS 3 (equivocal risk) category (F1 scores 0.53–0.75).
- 6Authors recommend further multicenter validation and larger datasets before clinical adoption.
Why It Matters
Automating PI-RADS classification could streamline prostate MRI reporting, reduce variability, and enhance workflow efficiency for radiologists. However, challenges in intermediate-risk lesion classification underscore the need for more robust validation before real-world clinical use.
Source
AuntMinnie
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