## 3 Clinical Applications of Artificial Intelligence  
### Imaging  
The main purpose of the use of AI and ML applications in imaging is to support the specialist in the diagnosis of diseases. Computer-aided diagnosis (CAD) is among the first applications of these new algorithms in the imaging area [32,33] and incorporates ML classifiers trained to distinguish lesions from normal tissue [34]. In lung computed tomography (CT), ML applied to combinations of CT textural features scored high accuracy in distinguishing malignant lesions [35] or invasive from minimally invasive lesions [36]. In the relatively recent radiomics approach, quantitative analysis of radiological images (mainly CT [37, 38, 39], magnetic resonance imaging (MRI) [40, 41, 42], and positron emission tomography (PET) [43] images, but also ultrasounds [44], mammograms [45], and radiography) by extraction of a large number of image features (up to a few hundred or thousands) can be combined with ML classifiers to produce prognostic and predictive models [39]. In image elaboration, DL algorithms can learn the structure labeling of each image voxel directly (semantic segmentation) in order to contour lesions or organs [46]. U-net, one of the most popular DL architectures for image segmentation, has proven to be capable of automatically segmenting lung parenchyma [47] and lung tumor using PET-CT hybrid imaging [48]. A cornerstone of optimization of clinical imaging protocols is patients' dose estimation, which allows the dose to be balanced with image quality. Dose to the patient can be automatically calculated by DL in CT [49], single-photon emission computed tomography (SPECT) [50], and PET [51]. In interventional radiology, DL has been proposed for skin dose estimation [52]. In chest CT, ML could be used to predict the volumetric computed tomography dose index (CTDIvol) based on scan patient metrics (scanner, study description, protocol, patient age, sex, and water-equivalent diameter (DW)) and identify exams, which hold potential for dose reduction by tuning the acquisition parameters [53]. Another pillar of patient dose optimization is image quality improvement, as it allows dose reduction for the same image quality. The integration of AI algorithms within the imaging technology allows for improving imaging quality and, consequently, to reduce patient dose. DL methods have been used for improving PET image quality, reducing noise [54], removing streak artifacts from CT [55], and developing novel techniques for tomographic image reconstruction based on a reduced amount of acquired data. Other promising applications are a generation of synthetic images, such as synthetic CT from MRI [56], virtual contrast-enhanced images [57], and rigid/deformable intramodal and multimodal image registration [58], and extraction of the respiratory signal [21] that could be used for breathing motion compensation of images [59]. In interventional radiology, AI can predict tumor response to transarterial chemoembolization based on image texture and patient characteristics [60,61]. In the future, real-time registration DL algorithms could be used to superimpose high-resolution preoperative MR imaging with intra-procedural fluoroscopy, guiding the physicians during the catheter's manipulation [62] for estimating ablation margins and helping minimize damages to structures close to the treated area. AI can be useful also in longitudinal studies during follow-up of treatments in order to detect subtle changes between images, thus identifying progress or recurrence at an earlier stage [63,64]. Ophthalmic imaging, e.g., fundus digital photography, optical coherence tomography, among other imaging fields, is where artificial intelligence can support the specialist in the diagnosis of ophthalmic disorders, such as diabetic retinopathy, age-related macular degeneration, and others [65].