# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
## 3 An Artificial Intelligence Approach to Precision Medicine
Therefore, discerning a causal correlation between miRNAs' expression and a coincidental one is impossible for clinicians or researchers. Here, AI becomes the key to this limitation. Moreover, AI can integrate patients' miRNA profiles with omics data to define a precise strategy for diagnosis and therapy [164; 165]. As reported before, EVs' miRNAs carry more personalized information with respect to the circulating miRNA. Here resides the indissoluble bond between AI and EVs. EVs' importance was recognized only in recent decades; therefore, AI applications to EVs' miRNA are still limited. However, it will have future applications. Therefore, we propose an overview of AI's applications with regard to miRNAs in BC for diagnosis and therapy. [PERSON] et al., using ML algorithms integrated with feature-selection methods, demonstrated that with just three selected miRNAs, classifiers can still detect breast cancer in comparison to the use of many more miRNAs [166]. Other studies report interesting results from integrating the data of circulating miRNAs using multiple classification algorithms, an ensemble of decision trees, neural networks, and support vector machines [167; 168]. In recent years, many models have been proposed to detect biomarkers in miRNAs, from a tree-based model to the most advanced algorithms such as deep learning (DL) or convolutional neural networks (CNNs), in different types of cancer, but their applications are still limited. On the other hand, machine learning algorithms are widely used in BC imaging diagnoses, for example, mammography [169; 170; 171; 147]. AI can detect breast lumps (masses), mass segmentation, breast density, and the risk of breast cancer [172]. However, many scientists have identified challenging issues in these techniques [173]. miRNAs can also be useful for classifying different subtypes of BC; several studies in the literature have shown a particular pattern of expression of miRNAs for each subtype [174; 175]. This is probably the most challenging field for clinicians and AI since miRNAs' expression could also be influenced by a patient's specific characteristics. Nowadays, few studies report the use of AI algorithms to detect miRNA as a subtype of biomarker [176]. Just as in BC early detection, the most common applications in BC subtypes' detection are related to clinicopathologic or imaging parameters. Other authors implemented a predictive model based on an advanced ML algorithm using clinicopathological variables. Using a powerful ML algorithm and a support vector machine (SMV), which is ideal for categorization tasks, with the Firefly algorithm, they achieved excellent results compared to other algorithms. They were able to identify TNBC and nonTNBC with high accuracy [177]. Furthermore, a radiomics-based model proposed showed promising results in BC subtype detection, with an accuracy of 0.902 [178]. Nevertheless, this field of AI still has many limitations and necessitates further study. Few studies report the application of AI in prognosis using an miRNA profile [178]. Despite the challenges presented, the exploration of AI in the analysis of miRNA for cancer holdsgreat promise as a research area. As AI technology continues to advance and larger miRNA expression datasets become available, it is poised to assume an increasingly significant role in the realms of cancer diagnosis, prognosis, and treatment. In the foreseeable future, improvements in AI algorithms and heightened collaboration among researchers, clinicians, and industry stakeholders are anticipated to further facilitate the seamless integration of AI into the routine management of BC. Finally, the amalgamation of miRNA analyses with other omics data has the potential to offer a more comprehensive understanding of BC biology, paving the way for the development of targeted therapeutic approaches in relation to other cancers.