AAI_2025_Capstone_Chronicles_Combined

high-throughput or under-resourced environments, where radiologist fatigue or time constraints increase the risk of overlooked abnormalities, these systems could serve as a valuable second read. While neither model was perfectly calibrated, both were effective at identifying pathologies warranting additional attention, and could be integrated into PACS systems with human oversight. Importantly, false positives in this context are acceptable and expected, as they feed into an existing workflow of clinical verification rather than acting autonomously. If we were to continue this project, our next steps would focus on model interpretability and broader generalization. For interpretability, integrating techniques like Grad-CAM could offer visual explanations for model predictions, making the system more transparent and trustworthy for clinical users. On the modeling side, we would consider ensemble techniques, combining EfficientNet with other backbone architectures or even fusing predictions with the hybrid CNN outputs. Additionally, augmenting the dataset with external sources or conducting domain adaptation for other hospital systems could improve robustness. To prepare the system for production, we would also need to conduct more extensive validation on unseen clinical data, assess latency and scalability constraints, and address ethical/legal compliance issues related to medical AI deployment. Future Work and Production Considerations​ ​

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