# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
###### Abstract  
The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocariers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated. artificial intelligence; machine learning; magnetic hyperthermia; magnetic nanoparticles; cancer treatment; drug delivery +
Footnote †: journal: _Future Internet_  
synergy of the drug depending on its dose, administration timing, and current treatment process. The latest development in nanotechnology enables the design of nanocarriers for targeted drug delivery, improving medicine release and beating cancer cells. In turn, manufacturing the nanoparticles, which can be loaded with drugs or other agents (stabilizers, compounds for diagnostics), is a time and financial outlays-consuming process. Thus, the AI-based prediction of the effect of nanoparticles with drugs on living tissues enables the development of targeted nanomedicine [8; 9]. One cancer treatment method that uses magnetized particles is magnetic hyperthermia [10; 11]. It can lead to the death of cells through the modulation of cellular processes by generating heat. It relies on intravenous injection or carrying of the magnetic particles at the site of the tumor's tissue and their heating in an alternating magnetic field in the range of temperature 40-43 \({}^{\circ}\)C as an effect of both hysteresis and relaxation losses [12]. Magnetized nanoparticles reach the tumor by disrupting the endothelial barrier with an external magnetic field [13]. This process provides the killing or weakening of tumor cells. Nanoparticles that can be used in Magnetic Resonance Imaging (MRI) imaging are super-paramagnetic iron oxide nanoparticles (SPIONs), fluorescent agents, perfluorocarbon, and gadolinium, while X-ray and CT use High-density gadolinium or iodine as contrast agents [14]. SPIONs-based magnetic hyperthermia may destroy malignant tissues in the body [15]. Thus, magnetic hyperthermia requires selecting the proper process parameters and the properties of magnetic particles; and conditions like the position and properties of the tumor, the health status of subjects, and current and taken therapies. Artificial Intelligence has been involved in making this process more efficient and predictable [5]. In [16], _in-silico_ modeling for hyperthermia using the bioinformatics tool i-TASSER was presented, namely evaluating the effect of amino acid sequences on metal binding sites.