Performance of ChatGPT-4o in Determining Radiology–Pathology Concordance and Management Recommendations Following Image-Guided Breast Biopsies
Bibliographic record
Abstract
Background: Determining radiology–pathology concordance after breast biopsies is critical to ensuring appropriate patient management. However, expertise and multidisciplinary input are not universally accessible. Purpose: To evaluate the performance of a large language model, ChatGPT-4o, in determining the radiology–pathology concordance of breast biopsies and suggesting subsequent management steps. Methods: A retrospective single-center study analyzed 244 cases of image-guided breast biopsies of women. ChatGPT-4o assessed de-identified radiology and pathology reports for concordance and recommended management. Radiologist assessments served as the reference standard with final surgical pathology and 2-year imaging follow-up serving as gold standards when applicable. Concordance rates, management recommendations, and diagnostic agreement with the gold standard were compared using statistical tests, including McNemar’s, chi-square, Fisher–Freeman–Halton, and Cohen’s kappa. Results: ChatGPT-4o achieved a concordance rate of 98.8% vs. 98.0% for radiologists (p = 0.625) and demonstrated high diagnostic agreement with the gold standard (kappa = 0.947, p < 0.001). ChatGPT-4o favored imaging follow-up more than radiologists (49.2% vs. 41.8%, p < 0.001) and surgical management less frequently (41.8% vs. 46.7%). Conclusions: ChatGPT-4o demonstrated diagnostic performance comparable to radiologists with breast imaging subspecialities in evaluating breast biopsy concordance. Its slightly more conservative management approach may enhance shared decision-making in resource-limited settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".