# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 4 Discussion
### Comprehensive Summary  
The study evaluated AI models, ChatGPT 4o and Gemini Pro, in analyzing single-phase contrast-enhanced MRI videos in frontal and sagittal planes of a child's head, focusing on identifying a medulloblastoma tumor. In the frontal plane, the first set of questions revealed that the model provided detailed video properties and suggested various analysis methods, identifying regions of motion and contours. The second set indicated that the model recognized the need for additional information for detailed analysis. The third set demonstrated the model's capability to identify potential tumor regions in the initial frames. In the sagittal plane, the first set showed the model's ability to provide detailed video properties and suggest analysis methods like frame extraction and contrast analysis. The second set detailed the model's extraction of key frames and description of each. The third set highlighted the model's identification of potential tumor regions, emphasizing the need for additional clinical information. Both AI models demonstrated the ability to analyze videos by extracting key frames and employing techniques such as object detection, motion analysis, and edge detection. They were able to proceed with further analysis when provided with additional details or specific queries about the video content. However, neither model successfully identified and marked potential tumor areas in the video frames, indicating challenges in detecting possible pathological regions. Differences emerged in frame extraction: the frontal plane model extracted fewer frames, limiting the scope of sequence analysis, while the sagittal plane model extracted more frames, enabling a more comprehensive review. The marking methods also varied, with potential tumor regions highlighted by green contours in the frontal plane and red rectangles in the sagittal plane. Moreover, the frontal plane model functioned more generally with less detailed clinical input, whereas the sagittal plane model provided more specific suggestions for additional clinical information. The integration of AI technologies, particularly ChatGPT, into medical imaging has shown significant promise, as evidenced by several recent studies. For instance, [PERSON] et al. highlighted ChatGPT-4's capabilities in ultrasound imaging, emphasizing its potential to reduce operator dependency and improve diagnostic accuracy in fields where expertise is limited. However, while ChatGPT-4 demonstrated strengths in ultrasound segmentation tasks, our findings underscore its limitations in handling more complex MRI video sequences, particularly in identifying specific pathological regions. This comparison suggests the need for specialized training and model refinement for different imaging modalities [18]. Similarly, [PERSON] et al. examined ChatGPT's performance in answering questions about MRI physics, revealing its difficulties in managing context-dependent and clinically complex tasks. Both our study and theirs indicate ChatGPT's proficiency in basic radiological concepts but highlight its challenges with advanced image interpretation, particularly in dynamic video content [19]. Moreover, [PERSON] et al. explored ChatGPT's role in diagnostic imaging, reporting similar limitations such as the fabrication of findings and the necessity for human oversight. This aligns with our observation of ChatGPT 4o's struggle to accurately identify neoplastic changes in MRI videos, further emphasizing the need for improved AI models in clinical tasks [20]. Lastly, [PERSON] et al. (2024) investigated a CNN and transformer-based approach that incorporated ChatGPT for better feature interpretation in MRI tumor detection. This study, similar to ours, highlighted the potential of integrating NLP with deep learning to enhance medical imaging analysis [21]. While both studies underscore the promise of AI tools, they also point to the critical need for continued refinement and development to meet the demands of complex medical data and ensure accurate diagnostics in clinical practice.