# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 4 Results and Discussion of Selected Articles
### Application Field Analysis  
Furthermore, we conducted a statistical analysis of the medical directions and diseases covered in these 45 articles. The applications are diverse, encompassing a total of 24 different diseases. Figure 6 displays the distribution of these diseases. In Figure 6, we observe that the disease directions covered in these papers include the following: atrial fibrillation and myocardial infarction, lung disease, diabetic retinopathy, fractures, classification of heart valves, human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS), HPV-related head and neck cancers, Parkinson's disease (PD), mental disorders, EEG analysis (Electroencephalogram), intracerebral hemorrhage (ICH), Coronavirus, Lyme disease, oral squamous cell carcinoma, muscular dystrophy, coronary artery disease, dental caries (tooth decay), breast cancer, glaucoma, malaria, sleep assessment, gastric cancer/endoscopic image classification, clinical gland morphometric information, and middle ear (ME) infection. These articles cover a wide range of medical conditions, highlighting the diverse applications of activation mapping techniques in the medical field. In Table 3, we can observe that CAM-based interpretability research provides strong support for medical image analysis and diagnosis in various fields, offering new possibilities to enhance patient care and diagnosis. Firstly, the field of cardiovascular diseases is one of the research hotspots for activation mapping methods, with a total of 10 articles focusing on this area. Cardiovascular diseases encompass a variety of cardiac and vascular conditions that often require rapid and accurate  
Figure 5: Publication years of articles using large dataset. diagnosis and classification. Therefore, the high interpretability and feature extraction capabilities of activation mapping methods make them powerful tools in this field. Secondly, pulmonary diseases, especially those related to COVID-19, have also attracted widespread attention from researchers, which is particularly crucial in the current global pandemic context. Seven articles focus on the detection of lung damage caused by the COVID-19 virus, highlighting the application value of activation mapping methods in crisis situations. They can expedite the diagnosis and classification of pulmonary lesions. Furthermore, the field of eye diseases has also benefited from research using activation mapping methods, particularly in the medical image classification and diagnosis of diabetic  
Figure 6: Distribution of diseases. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Medical Area** & **Related Diseases** & **Quantity** \\ \hline \multirow{4}{*}{Cardiovascular Diseases} & Atrial fibrillation and myocardial & \\  & infarction, classification of heart valves, & 10 \\  & coronary artery disease & \\ \cline{1-1}  & Lung disease & 7 \\  & Ophthalmic Diseases & Diabetic retinopathy & 6 \\  & Orthopedic Conditions & Fractures & 2 \\  & HIV and AIDS (MIC and AD) & 2 \\  & HPV-related head and neck cancer, oral & 3 \\  & squamous cell carcinoma, breast cancer & \\  & Parkinson’s disease (PD), mental & 3 \\  & disorders & \\  & Neurosurgery & Intracerebral hemorrhage (ICH) & 2 \\  & Infectious Diseases & Coronavirus, Lyme disease, malaria & 4 \\  & Musculoskeletal Disorders & Muscular dystrophy & 1 \\  & Oral Health & Dental caries & 1 \\  & Sleep Disorders & Sleep assessment & 1 \\  & Gastric cancer/endoscopic image & 1 \\  & Gastric Intestinal Diseases & Gastric cancer/endoscopic image & 1 \\  & classification & 1 \\  & Clinical Disease Research & Clinical gland morphometric information & 1 \\  & Ear, Nose, and Throat Diseases & Middle ear (ME) infection & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The areas of diseases in the included studies. retinopathy. This is a domain where precision is highly required. Activation mapping methods can help medical professionals more accurately identify and classify retinal lesions, facilitating early intervention and treatment.