# Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study
## 5 Discussion
Nevertheless, some performance bottlenecks were found. The study identified that visually similar digits, such as '2' and '5', present recognition challenges, particularly in the CNN-based classification, where minor variations in seven-segment structures led to increased misclassification. This suggests a limitation in the model's ability to differentiate fine-grained features in small objects. Future improvements should focus on handling ambiguous characters more effectively and improving model robustness through a more realistic data augmentation process. Overall, and looking at all results, this study suggests that YOLOv8l (640) achieved the highest detection accuracy under the given experimental conditions. However, it is important to note that these results were obtained using the specific dataset analyzed in this study, which reflects some controlled conditions and may not fully represent the variability encountered in real-world environments. Additionally, while YOLOv8l performs well in terms of precision and mAP@50, its inference speed is considerably slower than that of lighter models such as YOLOv8n. Therefore, its suitability may be limited in applications where real-time processing is a critical requirement. The comparative analysis highlights the trade-offs between accuracy and computational efficiency, indicating that model selection should be scenario dependent rather than based solely on peak detection performance. This study provides a foundation for the final smartphone application being researched in this project. Based on the findings, the most suitable model will be used in the next phase, which will include deployment and evaluation in real-world scenarios. Building on these findings, future research will focus on developing models capable of capturing contextual relationships between the detected elements. Future models will not only detect and classify individual digits and acronyms but also integrate contextual awareness to directly associate these values with their corresponding medical parameters (e.g., systolic or diastolic blood pressure), reducing the need for extensive post-processing. An additional consideration for future deployment is the integration of ethical safeguards, which was beyond the scope of this technical evaluation. As highlighted by [PERSON] [19], the increasing autonomy of AI systems in healthcare, especially when it operates with partial autonomy in diagnostic or monitoring tasks, introduces governance challenges that require transparency, accountability, and human oversight, which are not addressed by performance metrics alone. In line with these concerns, the concept of a \"Trustworthy Ethical Firewall\", introduced by [PERSON] [20], proposes a layered architecture embedding mathematically provable ethical constraints into the AI's decision-making core. This architecture ensures transparent, auditable decision-making, and incorporates escalation protocols to trigger human oversight in ambiguous or high-risk scenarios. Integrating such frameworks into medical AI systems could mitigate risks and promote trustworthiness, especially when AI agents are used for patient monitoring or diagnosis. Addressing these ethical aspects will ensure the safe and reliable deployment of the proposed future application.