# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
## 6 Discussion and Conclusions  
Recently, considerable effort has been made to apply AI to nanomedicine, including peptide synthesis, screening tests, prediction of drug toxicity, drug delivery, drug repositioning, polypharmacology, and physiochemical activity [88; 89; 90]. The prediction of the physicochemical properties of drugs requires the identification of the factors affecting the absorption, distribution, metabolism, and excretion of their compounds [90]. It is a promising direction; however, further research in drug delivery, biodistribution, and the long-term metabolism of nanomaterials in the human body is significant [91]. AI can help not only efficiently analyze large amounts of data but also predict the effects of further proceedings and has the potential to deliver targeted therapy, in particular, based on the application of magnetic hyperthermia that has been introduced clinically as an alternative approach for the tumor and cancer treatment [92; 93; 94; 95; 96]. AI-based algorithms provide the opportunity to optimize and accelerate nanofabrication's process of the materials. Prediction of the nanoparticle parameters, which impact the heat generation in MH, drug loading efficiency, biodistribution, or cytotoxicity might be crucial to propose more effective drug delivery systems [97]. AI algorithms have been applied to the analysis of the properties of the  
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\begin{tabular}{l l l l l l} \hline \hline
**Accuracy** & **Database (Type and Size if Available)** & **Input Parameters** & **Output Parameters** & **Reference** \\ \hline
\begin{tabular}{l} - \\  
nanoparticles [98], nonlinear relationships, factors that have influenced the stability and size of nanoparticles [99], or prediction of the delivery processes [100]. It can also provide feedback regarding metabolic pathways and the subjects' responses to therapies [101]. Thus, AI has the potential to accelerate not only drug and nanogenetics design [102] but also can boost progress in clinical trials. To summarize, the comparison of Artificial Intelligence-based algorithms, which can be applied to the prediction of nanoparticle properties or magnetic hyperthermia parameters in terms of their types and accuracy, data availability taking into account their content, types, and quality has been performed in Tables 1-7. It resulted that the application of AI-based techniques to the prediction of magnetic nanomaterials contributes to their higher efficiency, as well as the development of new materials. The efficiency of algorithms is related to the type of input data, their high dimensionality, and their nonlinearity [103]. AI-based algorithms enable us to effectively predict parameters such as the size of nanoparticles, effective thermal conductivity, the ratio of thermal conductivity, toxicity, cell viability, Young's modulus, ultimate tensile strength, reaction yield, quantum yield, optimal composition, specific absorption rate, compressive strength, swelling degree, and porosity. In the case of the prediction of the size of the nanoparticles, the neural network, which is based on multilayer perceptron, provides higher efficiency than ANNs, Table 1. In the case of multilayer perceptron, the analysis was made with more input data (a kind of parameter). ANNs provide higher efficiency than multilayer perceptron, SVM, and feedforward neural networks in the case of evaluating thermal conductivity issues, Table 2. Methods like Random Forest, Least Absolute Shrinkage Selection Operator Regression, Ridge Regression, Elastic Net Regression, and Support Vector Machine cannot predict the core size and exposure dose with high accuracy.