# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 2 Definition and Characteristics
### Characteristics  
AI applications in smart healthcare exhibit several characteristics, which can be summarized as the following three. (1) Big data processing and analyzing. There are several types of healthcare data, such as EHRs, image data (including X-rays), audio data (like heart and lung auscultation), and video data (such as physiological monitoring videos). These data types can be classified into two categories, structured data and unstructured data, depending on whether the information has a set data model or has not been organized in a predefined way [20]. The applications employ AI models to process and analyze these data to make optimal predictions and achieve continuous learning, and adaptability based on the continuous influx of new data. (2) Augmented intelligence. In the smart healthcare field, the prevailing view is that AI should collaborate with human intelligence. This collaboration could include making the delivery of accurate results easier and faster, thereby assisting doctors in decision making; aiding nurses in managing vast quantities of patient data; and advising and guiding physicians on administering proper treatment; among other benefits [21]. However, in cases where machines surpass human performance, where errors do not result in severe consequences, or in situations where doctors are unavailable and machines can perform effectively, full AI automation is possible [19]. (3) Software and hardware combined. AI applications in smart healthcare encompass not only software components such as ML, DL, and NLP, but also hardware components like medical imaging equipment, vital signs monitoring equipment, and surgical robots. These applications can be classified into two categories: the first category analyzes structured data, attempts to cluster patients' traits, and infers the probability of disease outcomes; the second category extracts information from unstructured data, converts it into machine-readable structured data, and enables analysis by the first category applications [22]. AI applications in smart healthcare typically consist of several components. In this section, we describe their common architecture and functionality. (1) Data collection and integration part  
In the smart healthcare field, hardware such as medical imaging equipment, vital sign monitoring equipment, and wearable devices, is combined with software like data management systems and data integration systems to collect and integrate patients' EHRs, medical imaging, physiological data, and more [23]. (2) Unstructured data processing part  
Approximately 80% of the data are unstructured and underutilized, including doctors' notes, medical images, and other audio and video recordings. To utilize these data, AI technologies such as ML, DL, NLP, or radionics, are employed to mine data, create or identify patterns in unstructured data, and extract structured data from these unstructured sources [24]. (3) Data preprocessing part  
Data preprocessing in the smart healthcare field involves preparing medical data such as medical images, medical text, and vital signs monitoring data. This includes operations such as filling in missing data, normalization, and removing noise and outliers from the original data. These steps improve model performance, ensure data consistency, and prepare the data for use by AI algorithms [25; 26]. (4) Algorithm and model part  
Several AI algorithms are widely used in the smart healthcare field for tasks such as spotting DNA mutations in tumors, predicting heart attacks, and assessing suicide risk. AI models such as CNNs, which are highly promising for medical image recognition [27]; RNNs (including LSTMs), suitable for handling time-series data such as electrocardiograms and medical notes [28]; and SVMs, preferred for their simplicity, explainability, and effectiveness in areas like disease prediction and medical diagnosis [29], are commonly employed.