# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 1 Introduction  
In the healthcare industry, artificial intelligence assists in medical diagnostics; artificial intelligence models based on deep learning have demonstrated their ability to assist in various medical diagnoses [1]. Specifically, deep learning has been utilized in diagnosing and treating diverse medical conditions, such as breast cancer detection [2], the assessment of thyroid nodules [2], and the analysis of diabetic retinopathy symptoms and progression [3]. However, transparency is often lacking in the use of deep learning within the medical domain. The primary reason for this lack of transparency is the complexity of neural networks, including the multitude of parameters, numerous hidden layers, and the non-arbitrarily selected characteristics of these parameters, which makes it challenging for individuals to understand how these systems produce results in tasks like image classification or localization. As a result, there is a lack of trust in the predictions and localization analyses they provide. In the explainable methods in the medical field, CAM (Class Activation Mapping) [4] stands out as a representative technique for visual explanation. Specifically, CAM, a technique in computer vision and deep learning, visualizes areas of an image crucial for a deep neural network's prediction of a specific class or category. The basic concept of CAM is to utilize gradient data from the last convolutional layer of Convolutional Neural Networks (CNNs) to generate a heatmap highlighting key regions in an image forclassification purposes. Specifically, in the last layer of the CNN, global average pooling (GAP) is applied to transform each feature map into an average value that indirectly reflects the impact of the class on the corresponding feature map. The weighted feature maps are subsequently aggregated, and the resulting heatmap represents important regions guiding the classification for each class in the neural network. However, CAM has limited applicability because it requires modifications to the model structure. Therefore, several advanced methods have been developed to overcome the limitations of CAM including Grad-CAM (gradient-weighted class activation mapping) [5], Grad-CAM++ [6], Ablation CAM [7], and Score CAM [8]. In the medical field, an extensive body of research has employed CAM-based methods to explain deep learning models. However, no systematic survey has been conducted to analyze the progress and challenges of CAM in the medical domain. Therefore, this study conducted a review of research on visual explanatory methods in the medical field from 2016 to 2023. Materials and Methods: Relevant literature using CAM-based explanation methods in the medical domain was retrieved through searches on three databases: PubMed, Science Direct, and Web of Science. Due to the initial proposal of the activation mapping interpretability method in 2016, only papers published from 1 January 2016 to 30 June 2023 are included. Specifically, papers were reviewed and evaluated according to publication year, medical relevance, neural network models, datasets used for model development, visualization explanation methods, and application types. In total, 45 articles were included in the review. This systematic review thoroughly outlines the use of visual explanation methods in medicine. The integration of visual explanation methods with CNN models has the potential to enhance the accuracy of medical diagnosis and increase the transparency of the diagnostic process. Our research revealed that among the 45 selected articles that used activation mapping explanation methods in the healthcare industry, 82% (37 articles) utilized the Grad-CAM method, while only seven articles employed methods proposed after Grad-CAM. These methods are relatively simple, and there is a lack of a unified standard for measuring the quality of these explainable methods.