Artificial Intelligence in Neuro-Oncology: Assessing ChatGPT’s Accuracy in MRI Interpretation and Treatment Advice
Bibliographic record
Abstract
Abstract Purpose Large language models (LLMs) have demonstrated advanced capabilities in interpreting text and visual inputs. Their potential to transform oncological practice is significant, but their accuracy and reliability in interpreting medical imaging and offering management suggestions remain underexplored. This study aimed to evaluate the performance of ChatGPT in interpreting T1-weighted contrast-enhanced MRI images of meningiomas and glioblastomas and providing treatment recommendations based on simulated patient inquiries. Methods This observational cohort study utilized publicly available MRI datasets. Thirty cases of meningiomas and glioblastomas were randomly selected, yielding 90 images (three orthogonal planes per case). ChatGPT-4o was tasked with interpreting these images and responding to six standardized patient-simulated questions. Two neuroradiologists and neurosurgeons assessed ChatGPT’s performance using five-point Likert scales and their inter-rater agreement was evaluated. Results ChatGPT identified MRI sequences with 91.7% accuracy and localized tumors correctly in 66.7% of cases. Tumor size was qualitatively described in 85% of cases, and the median acceptability was rated as 4.0 (IQR 4.0–5.0) by neuroradiologists. ChatGPT included meningioma in the differential diagnosis for 73.3% of meningioma cases and glioma in 83.3% of glioblastoma cases. Inter-rater agreement among neuroradiologists ranged from moderate to good (κ = 0.45–0.72). While surgical treatment was suggested in all symptomatic cases, neurosurgeon acceptability ratings varied, with poor inter-rater reliability. Conclusions ChatGPT demonstrates potential in interpreting neuro-oncological MRI images and offering preliminary management recommendations. However, errors in tumor localization and variability in recommendation acceptability underscore the need for physician oversight and further refinement of LLMs before clinical integration.
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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 | 0.026 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".