Background/Objectives: Large language models (LLMs) are revolutionizing the world and the field of medicine while constantly improving themselves. With recent advancements in image interpretation, evaluating the reasoning capabilities of these models and benchmarking their performance on brain MRI tasks has become crucial, as they may be utilized—albeit off-label—for patient care by both neurosurgeons and non-neurosurgeons. Methods: ChatGPT-4o, Grok, and Gemini were presented with 35,711 slices of brain MRI, including various pathologies and normal MRIs. Models were asked to identify the MRI sequence and determine the presence of pathology. Their individual performances were measured and compared with one another. Results: GPT refused to answer 28.02% of the slices despite three attempts, whereas Grok and Gemini provided responses on the first attempt for every slice. Gemini achieved 74.54% pathology prediction and 46.38% sequence prediction accuracy. GPT-4o achieved 74.33% pathology prediction and 85.98% sequence prediction accuracy for questions that it had answered (53.50% and 61.67% in total, respectively). Grok achieved 65.64% pathology prediction and 66.23% sequence prediction accuracy. Conclusions: The image interpretation capabilities of the investigated LLMs are limited for now and require further refinement before competing with specifically trained and fine-tuned dedicated applications. Amongst them, Gemini outperforms the others in pathology prediction while Grok outperforms others in sequence prediction. These limitations should be kept in mind if use during patient care is planned.

This article presents a new benchmark MRI dataset called the Gazi Brains Dataset 2020, containing MRI images of 100 patients, and introduces initial experimental results performed on this dataset in comparison with available brain MRI datasets. Furthermore, the dataset is analyzed using eight different deep learning models for high-grade glioma tumor prediction, classification, and detection tasks. Additionally, this study demonstrates the results of an explainable Artificial Intelligence (XAI) approach applied to the trained models. To demonstrate the utility of the proposed dataset, different deep learning models were applied to the problem, and these models were tested on various data and models applied for various tasks such as region of interest extraction, whole tumor segmentation, prediction, detection, and classification with accuracy, precision, recall, and F1-score. The experimental results indicate that the dataset is highly effective for multiple purposes, and the models reached significant results with successful F1-scores ranging between 93.2% and 96.4%. ROI and whole tumor segmentations were successfully performed and compared with seven algorithms with accuracies of 87.61% and 97.18%. The Grad-CAM model also demonstrated satisfactory accuracy across the tests that were conducted. Moreover, this study explores the application of XAI to the trained models, providing interpretability and insights into the decision-making processes. The findings signify that this dataset holds significant potential for various future research directions, including age estimation, gender detection, causal inference with XAI, and disease-related survival analysis.

Capillary hemangioma of the cavernous sinus is considered a benign vascular skull base tumor. In this case report, a patient with capillary hemangioma located in the cavernous sinus who was operated on with endoscopic endonasal approach is presented. What should be considered when planning the treatment for capillary hemangiomas located in the cavernous sinus? The treatment approaches applied in 12 patients with a diagnosis of capillary hemangioma located in the cavernous sinus, reported in the English literature, are reviewed in this article. We presented the clinical and radiological results of the treatments. Cavernous sinus capillary hemangiomas are considered benign tumors. However, surgical treatment is preferred in the foreground because of the neurological deficits due to the compression effect caused by the mass. In the case reported by Masman et al. for the first time in the literature in 2021, they provided total excision with the endoscopic endonasal approach in the patient they first diagnosed by taking a biopsy with the endoscopic endonasal approach. In our case report, we provided total excision with endoscopic endonasal transsphenoidal approach. Many factors should be considered when deciding on the treatment method in these patients. Although radiotherapy is quite effective in the treatment of capillary hemangioma, surgery should be preferred, especially in symptomatic patients. In patients who cannot undergo surgery, radiotherapy is very valuable, although the long-term results are not yet known.