# Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
## 3 Survey Results on Local Explanation in Industrial AI Applications
### Qualitative Analysis
#### 3.2.5 Healthcare  
Table 2 shows that in the healthcare sector, most research studies have focused on applying Grad-CAM, SHAP, and LIME methods.  
Numerous studies have utilized the SHAP method to interpret tabular data. For example, while [PERSON] et al. [81] developed an interpretable machine learning model for predicting early neurological deterioration related to stroke, [PERSON] et al. [80] developed an approach that captures non-linear feature effects in personalized risk predictions. [PERSON] et al. [82] developed models for diagnosing chronic kidney disease (CKD) that identified important features consistent with clinical knowledge. [PERSON] et al. [83] also used SHAP to develop machine learning models for CKD diagnosis that provided physicians with richer information on feature importance to improve decision making. These studies highlight the usefulness of SHAP in developing interpretable models for medical applications and the potential to improve clinical decision making.  
Three XAI approaches were used for time-series data: attention, layer-wise relevance propagation (LRP), and Grad-CAM. [PERSON] et al. [84] introduced HAN-ECG. This bidirectional-recurrent-neural network-based technique employs three attention mechanism levels to detect atrial fibrillation (AF) patterns in an ECG (electrocardiogram). However, the success of the strategy is dependent on the preprocessing stage. This method should be applied to different ECG leads and arrhythmias in the future to extract novel patterns that may be beneficial in detecting arrhythmias. An interpretability method was proposed by [PERSON] et al. in their paper [87] to explain DNN decisions for recognizing the movement preceding the freezing of gait (FOG) in Parkinson's disease (PD). The recommended pipeline can help physicians to explain DNN conclusions and let ML experts check the generalizability of their models. This pipeline could be used to start FOG treatment pipelines. In these circumstances, the interpreters can promote the provision of external stimuli and evaluate the effectiveness of the intervention by picturing diminished significance for FOG. [PERSON] et al.'s SleepXAI [88], an explainable unified method for multi-class sleep stage categorization utilizing modified Grad-CAM, was first proposed in 2012. This technique explains multi-class categorization of univariate EEG (electroencephalogram) signals while significantly increasing overall sleep stage classification accuracy. During the anticipated sleep stage, SleepXAI generates a heatmap depicting relevant features learned from univariate EEG data. It enables sleep specialists to link observed characteristics to traditional manual sleep grading techniques, boosting confidence in opaque systems with justifications.  
Various studies have applied the Grad-CAM method for interpreting image data, focusing on medical imaging. For example, [PERSON] et al. [97] proposed a two-stage training process to enhance classification performance and guide the CNN's attention to lesion areas. To detect COVID-19 on chest X-ray images, [PERSON] et al. [93] developed DeepCCXR (deep COVID-19 CXR detection), while [PERSON] et al. proposed a hierarchical neural network with spatial attention modules for skin cancer diagnosis. To help COVID-19 patients be triaged more quickly, [PERSON] et al. [95] proposed an AI-based solution; [PERSON] et al. [98] presented COMiT-Net; and [PERSON] et al. [103] proposed an explainable deep learning model. These investigations show that the model's decision-making process is now more accurate and comprehensible and can be directed to focus on particular areas of interest. However, limitations include the need for more accurate labeled datasets,limited data availability, and extending the models to process other types of diseases with radiography images.