# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
## 6 Discussion and Conclusions
At the same time, Random Forest provides good accuracy in the prediction of cell viability, Table 3. The optimal properties of nanomaterial performance can be done with ANNs and multilayer perceptron, while Random Forrest is not efficient for this purpose, Table 4. In turn, the prediction of power losses of magnetic particles can be successfully performed with ANNs and Random Forrest, Table 5. Least Square Support Vector Machine and ANNenables predict, with high accuracy, the swelling degree, Table 6. The removal efficiency can be successfully evaluated with ANNS, and an adaptive neuro-fuzzy inference system, Table 7.  
Since magnetic hyperthermia-based research are complex and often incorporates non-linear effects, the approach based on linear regression may need to provide higher prediction accuracy. Random Forest may give a higher accuracy as it enables non-linear learning effects, but this method tends to be instructive in the case of infinite single trees. In turn, SVM entails a high computational cost and needs to be better scalable for massive datasets. In contrast to regression and Random Forrest, SVM does not provide a probabilistic explanation for classification. Considering the computational cost, CNN minimizes this effort concerning regular neural networks. It also has a problem with the classification of images with different positions. The largest disadvantage of CNN is that it requires a considerable amount of good-quality data to be trained with high accuracy. However, each case is different, requiring an individual approach. Here, a significant limitation of AI in nanomedicine is the lack of well-described, heterogeneous databases [104], experimental data error in training sets, and experimental validations [105]. Primarily, the dataset for prediction of the nanoparticle's properties and their synthesis were collected from already published papers and experiments. As a response to massive datasets, the need for platforms for storing and sharing nanomedicine data development began [106]. These data should be categorized and standardized [107; 108]. The first attempts in this field have been made; for example, the database caNanoLab established by the National Cancer Institute (NCI) includes the design of 174 protocols for nanomaterials, which are coming from individual laboratories [109]. Other limitations are connected with converting diagnostic and therapeutic issues into measurable values [110]. It is also worth stressing the need to protect sensitive personal data, whose registration and storage involve personalized medicine [111]. Another important issue is connected with the security of confidential and sensitive data, including patients' medical information [94], which uses algorithms based on Artificial Intelligence. The data should be anonymized and subjected to the procedures of authorized information storage to avoid limitations of usage. The integration of the properties of the nanoparticles with computational modeling is an issue in the delivery of efficient, targeted therapies. The mutual interaction of drug, plasma, vascular endothelium, and cellular membranes can be optimized with Artificial Intelligence-based algorithms [112]. Thus, they can be considered an effective IT support tool for magnetic hyperthermia-based research. It contributes to optimizing the properties of the nanomaterials, predicting interactions with drugs and human bodies, the behavior of nanocarriers in vitro and in vivo, and the drug delivery processes. The framework for applying AI-based algorithms for magnetic hyperthermia-based research can be adopted from other fields. The papers reviewed here clearly illustrate the effectiveness of the approach proposed. In addition to the need for more standardization of data protocols, very often available metadata does not contain annotations. Thus, future directions include predicting optimal experiments, for example [103], and data curation initiatives, for example [108].