# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 5 Artificial Intelligence (AI) for Healthcare Delivery
### AI/ML Models Taxonomy Used in Healthcare Systems  
This section explains the application of AI/ML algorithms used in healthcare systems. AI is regarded as a broad family name that comprises ML reinforcement learning (RL), and DL as shown in Figure 3. AI \"is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages\" [123]. ML is \"the use and development of computer systems that can learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data\" [124]. ML is categorized into supervised learning (SL), unsupervised learning (UL), and semi-supervised learning (SSL) [125]. In SL, the algorithm learns from a labelled training dataset and makes predictions that are compared with the actual output values. If the predictions are not correct, then the algorithm is modified until it is satisfactory. This learning process continues until the algorithm achieves the required level of performance; examples of SL algorithms are SVM, NB, LR, etc. [126; 127; 128]. The UL algorithm does not require labelled data to learn and create a model. The algorithm is left unsupervised to find the underlying structure in the data to learn more and more about the data itself; examples of UL algorithms are k-means clustering and apriori algorithms (associations) [129; 130]. The SSL is a combination of supervised and unsupervised machine learning that uses a small amount of labelled data like SL and a larger amount of unlabelled data like UL to train the algorithms. First, the labelled data is used to partially train the machine learning algorithm, and then this partially trained model is used to pseudo-label the rest of the unlabeled data. Finally, the ML algorithm is fully trained using a combination of labelled and pseudo-labelled data [124]. Reinforcement learning algorithms (RL) learn optimal actions through trial and error. This means that the algorithm decides the next action by learning behaviours that are based on its current state and that will maximize the reward in the future. This is done using reward feedback that allows the RL algorithm to learn which are the best behaviours that lead to maximum reward. This reward feedback is known as a reinforcement signal [131; 132; 133; 134]. The DL is a subset of the AI family. It is based on learning by example, just like humans, using artificial neural networks. These ANNs are created to mimic the neurons in the human brain so that DL algorithms can learn much more efficiently; examples of DL algorithms used in healthcare systems are CNN, RNN, deep belief networks (DBN), recurrent CNN (R-CNN), etc. Moreover, cascading the DL and RL algorithms result in enhanced performance algorithms such as Deep Q-Networks (DQN) [123].  
The IoHT and personalized healthcare systems can be seen as the global support of the data collected from medical devices. According to [PERSON] et al. (2019), \"It is a large area that includes the blockchain for IoMT in medicine, wearable sensors, mHealth Things, IoT networks for healthcare, Big Data, ambient sensors, MEMS devices, robotics in the healthcare industry, mobile health systems, health informatics security, privacy methods, and AI. Regarding heart disease diagnosis, the primary usage of ML algorithms is the supervised learning models such as (NB, SVM, and MLP), and unsupervised such as (cluster analysis, and private data clustering schemes). In the predictive methods, the most used methods are CNN and KNN as presented in Figure 4. Besides, robotic surgery systems are relevant to support health professionals. Nevertheless, the non-autonomous technologies such as TORS and AESOP lead to several advantages as these methods provide less aggressive treatments and provide better results in terms of blood loss and a faster recovery\".