# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
## 4 Artificial Intelligence and Machine Learning Based Approach  
Artificial Intelligence is the creation of models and programs that simulate, at least partially, intelligence behavior. It enables machines to learn from experience, adapt to new information, and perform human-like tasks [51]. These activities are based on knowledge, data modeling, algorithm systems, and computing power development. AI-based approaches include Machine Learning, Natural Language Processing (NLP), and speech recognition. AI is slowly becoming an inseparable companion in most areas of people's lives, including autonomous vehicles, smart homes, and power grids. Many modern digital devices contain AI modules; even internet browsers are working on its principles to predict content based on our search habits with high efficiency, or provide capabilities spanning from sorting spam. Netflix applied AI to personalize movie offers. Also, nanomedicine, in particular magnetic hyperthermia-based research, may be beneficial with AI applications, especially throughout the increase of product quality and decrease of cost connected with their developments [5; 8; 9]. Recently, a huge effort has been made to apply AI-based solutions, including Machine Learning in magnetic hyperthermia-based research [51; 52]. In turn, Machine Learning is an automatic improvement of computer programs through experience [53]. It includes various types of approaches, starting from traditional statistical ones, like, for example, linear regression, in fact, estimation of the best-fit line, to complex neural networks, including Artificial Neural Networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), Support Vector Machines (SVM), and Least-Squares Support Vector Machines (LS-SVM). Artificial Neural Networks are networks of computer-created artificial neurons that imitate the functioning of the human brain through applied algorithms [54]. The commonly used type of ANNs is the Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) [55]. The first, as the input, used time series and sequential data, i.e., text or video, and the size of input and output may vary, while the second used spatial data, namely images, and the size of input and output is fixed. RNN is usually applied to NLP, speech recognition, image captioning, and text translation [56], while CNN is used in image recognition and classification [57]. Both types can be applied in deep learning [58], which increases the ability to processing of raw data in comparison to conventional ML techniques. Deep learning uses the backpropagation algorithm to evaluate parameters, which are used to compute each layer's representation based on the previous layer's model. Still, the most commonly used ML form is supervised learning, which is based on the minimization of the error between the desired pattern and output sources by modification of internal parameters, often called weights. Thus, the application of AI, in particular ML, in mag  
Figure 1: The general idea of magnetic hyperthermia combined with AI is applied to tumor therapies. netic hyperthermia-based research can be challenging, depending on the various types of materials and the amount and quality of available data. Artificial Intelligence and Machine Learning as Support for Magnetic Hyperthermia-Based Research and Prediction Properties of Nanoparticles  
Since each subject is different, and drug synergy gives a different output in an individual case, transforming Artificial Intelligence (AI) to nanomedicine enables the analysis of large data sets and the effective selection of the optimal therapy [51; 52]. It is essential in cancer therapy, particularly in the application of magnetic hyperthermia, to predict the optimal parameters of the process. AI includes various algorithms; in this paper, we reviewed the existing solutions in the area of research, which involve the use of magnetic hyperthermia, taking into account their effectiveness, type and size of data sets, input and output parameters, and application fields.