# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 4 Results
### Application Domains  
As shown in Figure 3, the absolute majority of recent XAI applications are from the health domain. For instance, several works have focused on different kinds of cancer prediction and diagnosis, such as skin cancer detection and classification [32; 41; 42], breast cancer prediction [43; 44; 45], prostate cancer management and prediction [46; 47], lung cancer (relapse) prediction [48; 49], and ovarian cancer classification and surgery decision-making [50; 51]. In response to the COVID-19 pandemic, significant research has been directed toward using medical imaging for detecting COVID-19 [52], predicting the need for ICU admission for COVID-19 patients [53], diagnosing COVID-19 using chest X-ray images [54], predicting COVID-19 [55; 56; 57; 58; 59; 60], COVID-19 data classification [61], assessment of perceived stress in healthcare professionals attending COVID-19 [62], and COVID-19 forecasting [58]. Medical imaging and diagnostic applications are also prominent, including detecting paratuberculosis from histopathological images [63], predicting coronary artery disease from myocardial perfusion images [64], diagnosis and surgery [65], identifying reasons for MRI scans in multiple sclerosis patients [66], detecting the health status of neonates [67], spinal postures [68], and chronic wound classification [69]. Additionally, studies have focused on age-related macular degeneration detection [70], predicting immunological age [71], cognitive health assessment [72; 73], cardiovascular medicine [74; 75], glaucoma prediction and diagnosis [76; 77; 78], as well as predicting diabetes [79; 80; 81; 82] and classifying arrhythmia [83; 84]. General management applications in healthcare include predicting patient outcomes in ICU [60], functional work ability prediction [85], a decision support system for nutrition-related geriatric syndromes [86], predicting hospital admissions for cancer patients [87], medical data management [88], medical text processing [89], ML model development in medicine [90], pain recognition [91], drug response prediction [92; 93], face mask detection [94], and studying the sustainability of smart technology applications in healthcare [95]. Lastly, studies about tracing food behaviors [96], aspiration detection in flexible endoscopic evaluation of swallowing [97], human activity recognition [98], human lower limb activity recognition [99], factors influencing hearing aid use [100], predicting chronic obstructive pulmonary disease [101], and assessing developmental status in children [102] underline the diverse use of XAI in the health domain. It is also noteworthy that brain and neuroscience studies have frequently been the main application (Figure 3), often related to health. For example, Alzheimer's disease classification and prediction have been major areas of focus [103; 104; 105; 106; 107; 108; 109], and Parkinson's disease diagnosis has been extensively studied [110; 111; 112; 113]. There is also significant research on brain tumor diagnosis and localization [114; 115; 116; 117; 118], predicting brain hemorrhage [119], cognitive neuroscience development [120], and detecting and explaining autism spectrum disorder [121]. Other notable brain studies include the detection of epileptic seizures [122; 123],  
Figure 3: Main XAI application domain of the studies in our corpus (including all the main domains mentioned in at least three papers). predicting the risk of brain metastases in patients with lung cancer [124], and automating skull stripping from brain magnetic resonance images [125]. Similarly, three pharmacy studies are related to health, including metabolic stability and CYP inhibition prediction [126] and drug repurposing [127; 128]. In the field of environmental and agricultural applications, various studies have utilized XAI techniques for a wide range of purposes. For instance, earthquake-related studies have focused on predicting an earthquake [129] and assessing the spatial probability of earthquake impacts [130].