## 2 Related Research  
### Al-Based Computer-Aided Diagnosis (CAD) System  
Medicine is one of the fields in which AI technology is widely applied. The importance of AI in medicine is increasing as the medical paradigm shifts from standard empirical treatment to disease prediction, prevention, and personalized treatment. Medical AI learns big data through machine learning and recognizes specific patterns to diagnose and predict diseases or suggest customized treatment methods [25, 26]. In particular, the development of software medical devices that read images generated by X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and endoscopy, thereby assisting doctors in diagnosis, is active. They can help solve the shortage of radiologists and the difficulty in ensuring the consistency of interpreting images because of the shorter diagnosis time and determining the disease using more data [27, 28]. The development of CAD systems to assist doctors in diagnosis based on AI and big data using medical images is active [29, 30, 31, 32, 33]. Figure 1 shows the image analysis and CAD system using deep learning.  
VUNO [34] developed VUNO-med, a CT image-based AI solution for solitary pulmonary nodule detection. The AI-based software learns medical data and helps doctors in diagnosis. The diagnostic and assistive technology has doctor-level accuracy through feature extraction of the CT images related to pulmonary disease and lung cancer. JLK Inspection [35] provides 37 solutions for diagnosis assistance through the 14 body parts and 8 image modalities. Typically, it provides the probability of 16 lung diseases and visualization of abnormal lesions through chest X-ray. Lunit is developing clinical and diagnostic technology with medical imaging through deep learning. Histopathological examination is important in determining the patient's surgery and follow-up treatment. It plays a role in helping doctors diagnose chest radiography by detecting lesions, such as lung cancer and lung metastasis, pulmonary emphysema, diffuse interstitial lung disease, tuberculosis, and pneumonia, from chest X-ray images. The company researches mammography technologies to improve breast cancer screening speed, detect and localize lesions, and predict malignancy probability [36]. Samsung SDS and Vintage Lab worked with the Samsung Medical Center to develop Doctor Answer, which analyzes health examination data and Electronic Medical Record (EMR) data using deep learning. Doctor Answer is software for predicting breast cancer occurrence and recurrence. It provides services of predicting the risk of disease recurrence by using the hospitalization and treatment information of breast cancer patients and recommending the appropriate additional precision examination and examination interval according to the individual risk level using the health examination information [37]. Development is also actively ongoing abroad. Google DeepMind is developing AI software that diagnoses more than 50 eye diseases by analyzing Optical Coherence Tomography (OCT). NVIDIA developed a deep learning-based standalone software medical device for cancer diagnosis to help doctors diagnose by providing the  
Figure 1: Image analysis and CAD system using deep learning.  
function to identify cancer cells in patient images and display them [38]. Moreover, China's Tencent has developed Miyinga, an AI-based software medical device for medical image analysis and CAD to help predict 700 types of diseases through cooperation with more than 100 major hospitals in China [39]. Although these CAD systems detect various lesions based on AI, there is a performance degradation problem because of unclear boundaries in the image due to the noise characteristics of the medical images. Therefore, deep learning studies on the detection and classification of medical image lesions are actively ongoing.