# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 4 Discussion
### Potential Reasons for Failure  
1. Lack of Medical Specialization in the Models: Models such as ChatGPT 4o and Gemini Pro are general language models not specifically designed or trained to analyze medical video images. Although they possess the ability to process textual information and some knowledge of medicine, their lack of specialization in medical image analysis results in low accuracy and inconsistency in interpreting MRI data. Large language models (LLMs), such as ChatGPT and Gemini Pro, while proficient in processing textual information and demonstrating some medical knowledge [22], lack specialization in medical image analysis, leading to potential inaccuracies in interpreting MRI data. It is recommended that specialized medical LLMs, trained based on authoritative medical databases that are human-validated, provide greater accuracy and completeness in medical fields [23]. Evaluations of ChatGPT 4o's performance on medical licensing exams show high proficiency in handling textual and visual questions, meeting positive criteria but demonstrating limitations in clinical assessment and prioritization [24]. Additionally, a study comparing LLMs with healthcare-specific NLP tools found that ChatGPT 4 performed similarly in some tasks but less accurately in others, highlighting the need for task-specific evaluation before implementing LLMs in medical contexts [25];
2. Technical Limitations in Video Processing: The technical limitations in video processing of ChatGPT 4o and Gemini Pro arise primarily from their focus on text and static image analysis and lack the advanced visual analysis algorithms necessary for complex MRI sequences [26; 27]. Although ChatGPT excels in natural language processing tasks, it encounters challenges in tasks such as summarization and commonsense reasoning [27]. Additionally, the explainability of results in language models like ChatGPT poses significant challenges, hindering their use in sensitive applications [28]. Integrating video analysis with text-based language analysis remains a technological hurdle that has not been effectively addressed, underscoring the need for further development and optimization of AI models like ChatGPT [27]. Overcoming these limitations is crucial for enhancing the models' capabilities for more comprehensive and integrated data analysis across different modalities;
3. Limited Ability to Understand Medical Context: Although these models have some medical knowledge, their ability to understand specific clinical contexts is limited. The inability to correctly identify the tumor in the video material may stem from their incapacity to apply medical context in analyzing MRI images. Research articles provide insights into the performance of AI models like ChatGPT in medical contexts. While ChatGPT 4o demonstrated high proficiency in handling text- and image-based questions on the Japanese Medical Licensing Examination (JMLE) [20], it is noted that the model had difficulties with clinical assessment and prioritization, indicating limitations in applying medical context in certain task scenarios. Similarly, a study evaluating ChatGPT's ability to diagnose keratinocyte tumors found that although ChatGPT 4 improved diagnostic accuracy compared to ChatGPT 3.5, it still had limitations in specific tumor identification [29]. Additionally, a study comparing ChatGPT with Google in diagnosing rare rheumatologic diseases highlighted ChatGPT's comparable diagnostic effectiveness but with significantly reduced query execution time, suggesting its practicality in clinical settings [30]. These findings collectively suggest that while AI models like ChatGPT possess medical knowledge, their ability to understand specific clinical contexts, particularly in tasks such as identifying tumors in MRI images, may still be limited, necessitating further improvements to enhance accuracy and reliability [31; 32];
4. Issues with Access to Sufficient Training Data: For AI models to be effective in analyzing medical images, they must be trained on large diverse medical datasets.