# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 6 Research Hotspots and Challenges
### Challenges  
Regarding the future research directions and challenges, the following summary is provided:  
(1) Lack of Quantitative Analysis of Explainable Methods  
The utilization of explainability methods in research has become widespread; however, a clear and quantitative analysis of how these methods enhance the explanatory power of models is lacking. Many researchers employ techniques such as CAM to extract features, enhance model efficiency, or identify model-improving characteristics. Despite their demonstrated positive effects, a standardized tool or unit for quantifying their performance is currently absent. Researchers typically assess the effectiveness of using CAM techniques by measuring the improvement in model performance. Yet, a definitive and quantitative measure for the explanatory power of CAM techniques is currently unavailable, making it challenging to objectively evaluate their performance. Additionally, comparing the interpretability of different explainability methods fairly is complex, hindering the continued development and iteration of activation mapping methods. This complexity stems from the fact that interpretability assessment not only relates to model performance but also encompasses factors such as consistency, effectiveness, and practical feasibility in real-world applications. Therefore, future research should prioritize the development of more accurate and standardized quantitative methods to comprehensively evaluate and compare the performance of various explainability techniques. This will contribute to the advancement of the field and underscore the importance of using interpretability techniques judiciously, while considering various factors for a comprehensive evaluation of their impact on the model. (2) Selection of Explainability Methods  
Multiple CAM methods are available, and selecting the most appropriate one is a primary focus of current research. Medical images vary in form under different instruments and disease conditions. Therefore, for disease-specific studies, the choice of the CAM method that yields the best results is a subject worthy of further exploration. In fact, among the articles selected in this paper, 82% of them exclusively used Grad-CAM as the explainability method without comparing it to other explainability methods. This phenomenon may be attributed to Grad-CAM's efficient performance and stability in practical research. On the other hand, it is possible that while other CAM methods may produce superior imaging results, they lack a standardized metric for evaluating the quality of explanatory imaging. The selection of CAM methods for specific disease studies is a complex and in-depth research topic. Choosing the optimal CAM method should take into account multiple factors, including the characteristics of the specific disease, feature extraction methods, classifier choice, dataset characteristics, and more. Thus, future research should focus on conducting systematic evaluations of CAM methods to establish a comprehensive reference framework that assists researchers in making informed decisions when selecting the most suitable CAM methods, thereby better serving the research needs of different diseases. This will contribute to the advancement of the field of medical image explainability research and enhance its application value in the medical field. (3) Imbalance Issue  
For a model, explainability is one of the factors that measures its performance and utility. Using CAM methods enhances the model's explainability. However, inevitably, while gaining model explainability, the introduced CAM methods also increase the model's complexity. The balance between the added complexity and the obtained explainability is a key point for consideration and research. How to address the tradeoff between model explainability and predictive classification performance so that we can provide explainability without sacrificing the model's accuracy is a challenge for the future.