In critical fields like medicine and healthcare, the transparency and comprehensibility of machine learning and artificial intelligence systems hold immense importance in garnering trust in their outcomes. Errors stemming from these systems, such as incor rect diagnoses or treatments, bear the potential for serious, even life -threatening repercussions for patients. C onsequently, Artificial Intelligence (AI) has emerged as a pivotal area of investigation, focusing on unraveling the opaque nature of intricate and challenging -to-comprehend machine learning models. While human expertise can enhance the accuracy of these models, comprehending the inner workings of these models during their training phase can prove challenging, if not impossible. In our research, we identified 211 articles published in 2023 and delved into 20 of them to examine the application of these meth odologies within the medical domain. Key words: Machine learning , Artificial intelligence , Neuroscience , Neural networks 1. Introduction Deep Neural Networks (DNNs) excel in image classification tasks, often outperforming human domain experts. In biomedical fields, there's a revived interest in employing AI techniques for imaging classification and segmentation [1]. Convolutional Neural Network s (CNNs) stand as leading models for image classification and segmentation [2], although encod er-decoder transformer architectures have also emerged [3 –5]. Image segmentation in medical imaging involves delineating regions of interest, like lesions, by lab eling every pixel to group similar constituent objects together. Classification assigns labels such as benign/malignant or normal/pneumonia/COVID -19 to medical images for binary or multi -class problems. Biomedical images come from diverse modalities like X -rays and ultrasounds, crucial for disease diagnosis and management (e.g., using fundus images for detecting eye diseases) [6]. The democratization of AI allows domain experts to apply machine learning algorithms without extensive knowledge of underlying a lgorithms. AI aids biomedical imaging by discerning intricate details (e.g., tumor size, textu re) superior to human observers like radiologists [7], potentially transforming radiological diagnosis [8]. The brain, housing over a hundred billion neurons with myriad synapses, is the body's most complex organ. Brain tumors, notably gliomas, pose signif icant health threats, with increasing morbidity. Gliomas encompass various types originating from glial cells and present grave 2 symptoms like seizures, headaches, and behavioral changes [9 -11]. Accurate localization and recognition of gliomas through brain tumor segmentation significantly impact diagnosis, treatment planning, and prognosis [12 -14]. Decades of research have unraveled the biological and molecular und erpinnings of gliomas, advancing diagnostic and therapeutic methods. Understanding genetic cha nges informs brain tumor grading, heredity, and targeted therapies using genomic data. Imaging techniques, especially MRI, play a pivotal role in diagnosing and m onitoring brain tumors [13,14]. MRI - based examinations guide surgical interventions like minim ally invasive surgery, enabling precise tumor removal while minimizing damage to healthy tissue. Brain tumor segmentation, altering MRI representations to pinpoin t multiple tumor regions, aids prognosis and survival prediction, typically classifying tumor regions into Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) [15 -20]. This review aims to introduce common techniques, elucidate fundamental concepts, and exemplify neuroscientific research applications in brain tumor imaging. 2. Literature review 2.1.Artificial intelligence The term "artificial intelligence" (AI) lacks a pre cise definition and generally refers to computer systems crafted to imitate hum an intelligence, striving to perform tasks within the human capability. AI resides within computer science but intersects closely with diverse research domains like data science , machine learning, and statistics. The potential of AI in scientific fields la rgely stems from its knack for unveiling structures within extensive datasets and leveraging these insights to make predictions or execute tasks. These systems possess unique st rengths that complement human abilities. For instance, AI adeptly identifies pa tterns in intricate, high -dimensional data, serving as a powerful aid rather than a replacement for human researchers. The majority of modern AI systems rely on various forms of artificial neural networks (ANNs), drawing inspiration from the organization o f the nervous system [21 - 22]. Subdivisions within artificial intelligence align with intended applications (diagnostic versus predictive), required inputs (supervised versus unsupervised learning), or the mathematical foundations of approaches used (linear versus non -linear, symmetric versus non -symmetric). Diagnostic versus predictive AI involves distinguishing whether an AI, when analyzing an image of a mole, can dete rmine if it's cancerous or likely to become cancerous. Specialized techniques exist for b oth scenarios, though the overarching principles and challenges remain constant. An exploratory approach aiming to define mole subtypes might employ unsupervised learn ing, discovering groups without predefined categories, which may or may not correlate with benign or malignant properties. Conversely, a supervised approach actively seeks to differentiate cancerous from benign moles, learning from labeled datasets categor ized as "cancerous" or "noncancerous" [23]. 2.2. Unsupervised learning Unsupervised learn ing encompasses a range of techniques that operate independently of predetermined outcomes. For instance, modern evolutionary trees rely on clustering genetic 3 similari ties, where the final tree structure was initially unknown and solely emerges from the algorithm's underlying principles. These methods aim to uncover patterns of symmetry, order, or inherent structure within data [24]. Commonly used unsupervised learning methods include dimensionality reduction techniques like principal component analysis [25 ], hierarchical clustering such as Ward or Neighbour - joining [26 -27], fast and heuristic algorithms like k -means ideal for large datasets [28], and density -based appro aches like DBSCAN (density -based spatial clustering of applications with noise) [29]. The re's no inherent superiority among these methods; their performance depends on the underlying structure of the data. The choice of method is contingent upon the specif ic characteristics and nature of the dataset being analyzed. 2.3. Supervised learning Supervised learning relies on a labeled training dataset with known outcomes or classifications, such as images of moles paired with information regarding their malignanc y. While it excels in uncovering non -linear and non -symmetrical relationships, its core c oncept of optimizing a function using a given training dataset can lead to overfitting or bias adjustment issues that may not be immediately apparent [30]. In its simp lest manifestation, supervised machine learning can take the form of multiple linear regression, which can then be substituted with regularized linear regression models for controlling model complexity, or nonlinear regression models when dictated by the d ata's nature. The recent substantial advancements in various fields, including facial det ection, complex image processing tasks, and protein folding, along with foundational technologies like ChatGPT, have largely stemmed from the utilization of complex, n on-linear, and non - symmetrical methods. Examples of such methods include Support Vector M achines [31], k - nearest neighbors [32], Hidden Markov Models [33], and Markov Chain Monte Carlo methods [34]. These techniques are founded on distinct mathematical mod els and assumptions, each demonstrating excellence in diverse domains. Among supervised m achine learning models, neural networks [35] have garnered immense attention, especially with the evolution of Deep Learning [36], a variant of neural networks. The fo cus on neural networks has been propelled by their widespread applicability and performan ce in various tasks, making them a cornerstone in the domain of supervised learning. 2.4. Neural networks and deep learning Neural networks, developed to mimic biological processes akin to evolutionary algorithms, comprise interconnected nodes (perceptrons ) organized into layers —input, hidden, and output —forming the simplest form of this model [37]. The network's complexity, and the potential for overfitting, is determined by the number of nodes in the hidden layer. Each node, akin to a synapse, forwards a signal based on input signals, similar to a post -synaptic potential either reaching the activation threshold at the axon hillock or not. Recent breakthroughs in machine learning have largely been propelled by deep learning, a technique leveraging neural ne tworks with multiple hidden layers and a vast number of nodes ranging from hundreds to billions. Each layer serves as a level of abstraction, and the 4 algorithms enable the network to autonomously determine and refine its structure [38]. Deep learning has s urpassed many traditional AI techniques and underpins numerous remarkable achievements, such as the general -purpose utilization seen in models like ChatGPT and its successor, GPT -4 [39], as well as complex scientific tasks like protein folding [40]. Howeve r, despite its successes, deep learning isn't a universal solution; in many real -world scenarios, classical machine learning models, such as tree -based models, can outperform them depending on the dataset structure [40]. 3. Previous studies This section co nducts a comprehensive statistical analysis for brain tumor segmentation methods. In our study, we identified 211 articles published 2023 and analyzed 20. Table 1: Summary of previous research Authors Method Authors Method 1 Chun Chou et al [42] PBPK 11 Zhang et al [52] Deep Learning 2 Monirul et al [43] MobileNet 12 Farajzadeh et al [53] Deep Learning 3 Zhang et al [44] Machine learning 13 Ren et al [54] ANN 4 Anaya -Isaza et al [45] Cross - Transformer 14 Ruba et al [55] Deep Learning 5 Sharma et al [ 46] CNN 15 Ranjbarzadeh et al [56] CNN 6 Tripathy et al [47] CNN 16 Fernando & Toskos [57] Deep Learning 7 Patil & Kirange [48] SCNN 17 Raghuram & Hanumanthu [58] SDNN 8 Chakrabarty & Mahajan [49] CNN 18 Talukder et al [59] Deep Learning 9 Alemu et al [50] SVM 19 Shyamala & Brahmanada [60] Regression Neural Network 10 Yaqub et al [51] ALCResNet 20 Rezai [61] CNN 5 Figure 1 : Frequency of using each method 4. Conclusions Since the onset of AI research in the mid -20th century, the human brain has b een a fundamental source of inspiration for crafting artificial intelligence systems. This stems from the belief that the brain serve s as a tangible model of comprehensive intelligence, exhibiting abilities like perception, planning, and decision -making, m aking it an enticing blueprint for AI design. This review, based on discussions from the 2020 International Symposium on Artificial I ntelligence and Brain Science, delves into how mechanistic, structural, and functional elements inspired by the brain are u tilized to innovate and enhance existing AI systems. Specifically, this approach has led to the creation of sophisticated high -dimens ional deep neural networks, incorporating hierarchical architectures reminiscent of those observed in the brain. These netw orks showcase remarkable capabilities in tasks such as visual object recognition and memory -based cognitive functions. Furthermore, a dvancements in AI have contributed to progress within the realm of neuroscience. This article aims to introduce the latest methods in brain tumor detection, leveraging insights from AI and its intersection with brain -inspired approaches to improve diagnost ic techniques in this critical medical field.