# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 1 Introduction  
In this age of information, massive amounts of data are continuously generated across various fields. Concurrently hardware technology has made it possible to process these data and derive valuable insights, which has fueled the rapid development of AI across numerous domains. Particularly in the healthcare field, people's pursuit of high-quality and efficient healthcare, along with the pressure on healthcare resources due to the growing population, has spurred the widespread integration of AI into healthcare. This integration has resulted in applications such as disease prediction and prevention, diagnostic imaging, and personalized treatment plans. The field encompassing these applications is smart healthcare, which originated from the concept of 'Smart Planet' proposed by IBM (Armonk, NY, USA) in 2009 [1]. Behind various types of AI applications in this field lies a common essential idea: after data are collected, ML (DL) techniques interpret them and present predictive analytics [2]. Smart healthcare differs from traditional healthcare because traditional healthcare follows a specialist-centered approach, while smart healthcare adopts a patient-centered approach by integrating AI technology [3]. In this approach, patients' needs, experience, and engagement are placed at the core of medical services through the incorporation of modern and intelligent healthcare solutions, thereby building a smart healthcare system. There are several successful commercial implementations of smart healthcare. Merative L.P. provides smart healthcare products and services to nine of the top ten US hospitals [4]. Tempus collaborates with over 50% of all academic medical centers in the US [5]. Aidoc analyzes 2 million patients each month [6]. PathAI is utilized by 90% of the top 15 BioPharma companies to transform pathology [7]. Consequently, much research has focused on the applications and challenges of smart healthcare. Some research concludesthat the future of smart healthcare is bleak due to the challenges of transforming data into wisdom [8]. In contrast, other researchers believe that smart healthcare could help solve challenges such as the shortage of medical personnel, aging populations, and high healthcare costs, while also facilitating sustainable development [9]. The opportunities that AI offers to the smart healthcare field are threefold: (1) predictive healthcare modeling, which involves processing and analyzing data at scale, recognizing patterns, detecting anomalies, making dynamic predictions, and supporting decision making; (2) discovering knowledge from structured and unstructured data, and achieving adaptive learning and continuous improvement; (3) providing cross-domain insights, facilitating research and innovation, and enhancing resource utilization [10]. Although the concept of smart healthcare is relatively new, there have been numerous real-world AI applications in this field, ranging from disease prediction and prevention [11] to drug discovery and development [12], and robotic surgery [13]. We categorize real-world AI applications in the smart healthcare field into ten domains, as outlined in several papers. For each domain, we introduce the specific AI technologies used and summarize the advantages of these technologies. There are several challenges faced by AI applications in the smart healthcare field. Existing research analyzes the obstacles and shortcomings in each of the application domains [14]. In our review, we classify the challenges identified in these papers into ten categories and discuss each challenge and its existing solutions separately. Some of the challenges are common across several application domains, so research findings on these challenges will benefit all of them. The rest of this article is organized as follows. Section 2 summarizes the definition and characteristics of smart healthcare. Section 3 examines the opportunities presented by AI in smart healthcare. Section 4 categorizes smart healthcare applications into different domains.