# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 2 Background
### Advanced Technology Based on AI  
Machine Learning has made great advances in pharmaceuticals and biotech efficiency. With mHealth technology, patients can stay connected to their doctors to monitor infectious diseases such as COVID-19. Based on video processing, diagnosis is also possible without a contact device [28]. According to prior research, AI technologies applicable in the medical field can be classified into four technologies as shown in Figure 2.  
The first is AI for diagnostics. Diagnosing diseases takes years of medical training. This technology is time-consuming, putting doctors under strain and often delaying life-saving patient diagnostics [29]. Machine learning, especially deep learning algorithms, have recently made huge advances in automatically diagnosing diseases, making diagnostics cheaper and more accessible [30]. These algorithms can learn to spot patterns similar to the way doctors see them. A key difference is that algorithms need a lot of concrete examples that need to be neatly digitized, such as detecting lung cancer or strokes based on computed tomography (CT) scans, assessing the risk of sudden cardiac death or other heart diseases based on electrocardiograms and magnetic resonance imaging (MRI) images, classifying skin lesions in skin images, and identifying indicators of diabetic retinopathy in eye images. Since there are plenty of good data available in these cases, algorithms are becoming just as good at diagnostics as the experts. The difference is that the algorithm can draw conclusions in a fraction of a second, and it can be reproduced inexpensively across the world [31; 32]. The second classification is the individualization of treatment. Different patients respond to drugs and treatment schedules differently. Therefore, individualized treatment has enormous potential to increase patients' life spans. However, it is very hard to identify which factors should affect the choice of treatment [33]. AI can automate this complicated statistical task and help discover the characteristics that indicate or predict that a patient will have a particular response to a particular treatment. AI algorithm-embedded systems cross-reference similar patients and compare their treatments and outcomes. The resulting outcome predictions make it much easier for doctors to design the right treatment plan [34]. The third classification is AI in medical imaging, especially, imaging devices using a digital twin. A digital twin is a computer-modeled replica of a physical person. In healthcare, digital twins can be used to model both medical devices and patients to see how the former work on people with specific conditions as types of virtual images. Disease detection, classification, and quantitative assessment in medical imaging are important for early diagnosis and treatment planning [35]. Several techniques have been proposed for segmenting medical image data through quantitative assessment. However, some quantitative methods of evaluating medical images are inaccurate and require considerable computation time to analyze large amounts of data. Analytical methods using AI algorithms can improve diagnostic accuracy and save time [36]. The  
Figure 2: Artificial intelligence (AI) for diagnostics. fourth and final classification is AI as a telemedicine-assisted function. The increase of telemedicine--the remote treatment of patients--reflects the rise of possible AI applications. AI can assist in caring for patients remotely by monitoring their information through sensors. A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using AI algorithms that alert physicians on any issues. However, there are some issues about the limitations of monitoring in order to respect a person's privacy. Nevertheless, it is true that AI algorithms are essential for telemedicine activation in the COVID-19 era [37, 38].