# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 6 Research Hotspots and Challenges  
### Research Hotspots  
The application of activation mapping methods in the healthcare industry has shown a steady increase over the years. This article discusses the specific hotspots of its application in the following sections:  
(1) Disease Domain Hotspots  
Currently, activation mapping interpretative methods are increasingly used in the healthcare industry. In specific disease domains, they are particularly prevalent in cardiovascular diseases, lung diseases (especially COVID-19), and eye diseases (especially diabetic retinopathy, DR). Firstly, cardiovascular diseases often impact the electrophysiological characteristics of the heart, leading to abnormal changes in cardiac electrical signals. Activation mapping methods are valuable in this context, as they not only facilitate the rapid identification of features for classifying cardiovascular diseases but also provide significant assistance in enhancing the model's interpretability. Once important features are identified, they can also contribute to the model's accuracy in disease classification. Secondly, the application in lung diseases is second only to cardiovascular diseases. This can be attributed to the impact of COVID-19, which has swept the globe, making activation mapping methods widely applied in the diagnosis of the COVID-19 virus. Additionally, these methods have proven effective in the classification and diagnosis of lung diseases using CT images. While activation mapping methods have found some application in other disease domains, it is relatively limited. This may be due to the diverse nature of medical images and the limited information that can be obtained through images for diagnosing certain diseases. (2) Usage Method Hotspots  
CAM technology, as a vital tool for interpreting medical images, presents visual information in the form of heatmaps, providing an intuitive way to accurately identify regions of interest in medical images. This particular feature is one of the key hotspots for the current application of CAM technology in the healthcare industry. This method not only aids in pinpointing the specific location of potential abnormalities in images but also offers robust support for observing and improving deep learning models. Additionally, CAM technology plays a crucial role in model validation, performance evaluation, and comparison. Through activation mapping, we can verify the accuracy of models in classification or segmentation tasks, assess their classification effectiveness, and examine their interpretability. CAM technology has excelled, particularly in medical image classification and segmentation tasks, where it addresses the opacity issues of neural networks. Some researchers use traditional CAM methods as the foundation for new activation mapping techniques, especially in specific medical imaging fields, such as lung CT scans. Furthermore, CAM technology plays an important role in improving model accuracy. Through CAM technology, we can accurately identify diagnostic features relevant to disease classification. This is particularly significant in medical diagnostics that require real-time decision-making and feedback. Efficient models can generate decisions more rapidly, offering timelier medical assistance and recommendations. This approach is of crucial importance in enhancing the accuracy of medical image diagnosis and increasing the efficiency of medical decision-making, which holds the potential to drive advancements and innovations in the field of medicine. This is also one of the current research hotspots. (3) Large-Scale Data Research  
Based on trends observed in the literature, the selection of datasets in research papers is increasingly leaning toward the use of large datasets. This trend is driven by a combination of factors. Firstly, advancements in medical image acquisition technology have resulted in higher-quality medical images, creating a more feasible foundation for the application of large datasets.