# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 7 Conclusions  
AI-assisted diagnosis is now widely applied in the medical field. These technologies help medical professionals quickly access patient health information and expedite the diagnosis and treatment planning process, thereby increasing the efficiency of healthcare providers. Additionally, they provide highly accurate disease diagnoses, particularly benefiting patients in underserved medical areas, at a relatively low cost, ensuring timely treatment recommendations tailored to their medical conditions. This selection of 45 research articles demonstrates the broad scope of activation mapping methods in the medical industry, covering a wide range of diseases. In these 45 articles, although methods like Hires-CAM, Layer-CAM, Grad-CAM++, XGrad-CAM, and Score-CAM are mentioned in some studies, Grad-CAM remains the most frequently used activation mapping method in the medical industry. The specific applications of CAM methods are mainly reflected in the following aspects. CAM Technology as a visualization tool: CAM technology, with its heat map visualization, provides an intuitive means to identify critical areas within medical images. This aids in locating potential pathological regions and supports observations to enhance model performance. Validation and Evaluation Using CAM Technology: CAM technology serves as a tool for model validation, evaluation, and comparison. Activation mapping allows verification of the accuracy of model classification or segmentation, assessment of classification performance, and an evaluation of the feasibility of interpretability. Obtaining Interpretability Through CAM Technology: Leveraging the black box nature of neural networks, CAM technology offers a straightforward and efficient approach to achieving interpretability in medical image classification and segmentation tasks. Innovative Applications of CAM Technology: Some researchers build upon traditional CAM methods to develop new activation mapping methods, particularly in specific medical imaging domains such as lung CT scans. Additionally, the characteristics of CAM technology can be utilized for feature selection, showing promise in tasks that require neural network-based feature selection. In selecting a CAM method for model interpretation, it is imperative to undertake a comprehensive evaluation of the model's architectural framework. Empirical evidence from our experiments underscores the notion that disparate models exhibit differential responsiveness to various CAM methodologies. This necessitates an in-depth consideration of each model's unique architectural nuances and inherent characteristics. The congruence between a model's structural attributes and the specificities of a CAM method can significantly influence the efficacy of the interpretative outcomes. Hence, the adaptability of a CAM method to a particular model's architecture plays a pivotal role in facilitating optimal model interpretation, a finding that is crucial for advancing the precision and utility of CAM-based analytical approaches in complex modeling scenarios. In the medical industry, CAM explanation methods are increasingly focused on large datasets, covering a wide range of application areas. Cardiovascular and pulmonary diseases show relatively mature application areas. However, the absence of standardized metrics to measure the effectiveness of interpretability remains a challenge, necessitating the development of equitable evaluation standards among various CAM methods. The balance between model interpretability and predictive classification performance requires increased attention. Finally, the clinical significance of CAM methods in the medical field and how to maximize their explanatory capabilities are topics deserving careful consideration. Conceptualization, J.C.; methodology, J.C. and D.T. ; software, L.R. and D.L. ; validation, H.Z. and X.W. ; formal analysis, J.C., X.W. and L.R. ; investigation, J.C., X.W. and L.R. ; resources, D.T. ; data curation, J.C.; writing--original draft preparation, J.C. and D.T.