Artificial intelligence has started a revolution in the field of health and especially in brain diseases; indeed, they have paved the way for the rapid and non-invasive study, treatment, management and prediction of disease. Furthermore, advances in image processing technology have led to more and more cost-effective and low-risk analysis. Artificial intelligence for brain diseases allow doctors to perform noninvasive assessments of brain structure and infer the cause of abnormal function. For example, it is estimated that the daily error rate and variation in radiology is greater than 3%–5% .This requires new methods to help doctors analyze data efficiently and effectively.

Among these approaches, Machine Learning (ML) technique is now well known and widely used to solve brain related problems. ML is a subset of AI algorithms that automatically “learn” to identify categories or forecast unknown or future conditions starting from data. Several solutions have been developed over the years and many of them still provide successful results in the analysis and processing of brain data.

Quantitative and qualitative studies of normal and pathological structures are often part of clinical tasks where ML has achieved the most promising results in diseases such as dementia, schizophrenia, multiple sclerosis, cancer, and infectious and degenerative diseases. Furthermore, approaches to segmentation and detection of brain structures, as well as pathological tissues, are also extensively studied. However, it should be noted that, because of the complexity and amount of brain data, ML methodologies often include several steps to actually perform a task. For example, image preprocessing, feature selection and ranking, and resizing are often required as initial stages to increase algorithm performance to a suitable level. In recent years, a subfield of AI, Deep Learning (DL), has revolutionized many types of neuroanatomy tasks. In particular, DL algorithms have risen to a prominent position in computer vision, outperforming other methods on some high-profile image analysis standards. Unlike traditional ML models, in DL, useful representations and features are automatically learned, directly from the raw data, overcoming the problem of manual computation and selection of possibly relevant attributes. Thanks to significant advances in computing power, including the use of Graphics Processing Units (GPUs), such algorithms are beginning to be used effectively to learn from the typical 3D and 2D images of the medical field.

Healthcare AI uses computational algorithms and software to analyze complex structured and unstructured medical data to simulate human perception. This can be used to gain useful insights and predictions, allowing for a better understanding of disease progression and thus facilitating early disease detection. AI techniques include machine learning (ML) and natural language processing (NLP).

Machine learning ML is an AI technique that focuses on building computer algorithms that learn from data and automatically improve their accuracy over time. An algorithm is a series of statistical processing steps ‘trained’ to find patterns and features in big data in order to make decisions and predictions based on new data. ML can be supervised or unsupervised. In addition, deep learning (DL) techniques can also be used to support data analysis.

In supervised learning data is pre-labelled and the algorithm learns to associate input features from various data streams to best predict the labels. This trains the algorithm to learn to associate features from the new dataset with a particular label. The algorithm can then be used on unlabeled test data to make predictions about output values. We used the images extracted from the X-ray machine for use during the labeling process and cleaned the image data to label brain-related cases. The algorithm can then be applied to new, unlabeled radiographs to predict brain diseases. A wide range of supervised ML algorithms are available, many of which come from traditional statistics such as decision trees, logistic and linear regression.

In unsupervised learning, the input data is unlabeled and no known outcomes are provided in the model. In this case, the algorithm extracts meaningful features such as patterns and relationships in the data that humans would miss. A commonly used unsupervised learning technique is clustering, where highly complex data can be organized into similar groups to extrapolate algorithmic relationships. For example, data from various biomarkers obtained from brain, genetic, plasma and cerebrospinal fluid imaging samples can be analyzed using internal clustering techniques to uncover diagnostic markers. ‘hidden’ of brain diseases.

Deep learning is a subset of machine learning that uses algorithms to define artificial neural networks designed to learn how the human brain learns. The DL method uses an artificial neural network (ANN) to process data through many hidden layers in the neural network. It requires a large amount of data going through many layers of computation to adjust and improve the results without manual feature extraction. Deep learning is suitable for multidimensional data such as information in electronic medical records and medical images. A Convolutional Neural Network (CNN) is an example of a deep neural network that has been used in neural image analysis.

Natural Language Processing (NLP) is a branch of AI that uses computer-based methods to analyze language, text, and speech. NLP combines computational linguistics with statistical models, machine learning, and deep learning that allow computers to process human language (e.g. text or speech) and ‘understand’ the full meaning of it. it. NLP methods can be used to extract information from unstructured data (e.g. clinical notes, recorded patient conversations) into a structured format to allow analysis using ML techniques.

Data to be examined under ML/DL must go through a bunch of preprocessing steps in order to transform the raw data into machine readable data and to prepare it to undergo feature extraction. Analysis of data that has been collected is done based on certain characteristics called features. The features being considered must have the ability to discriminate and must be non-redundant. This way the training time and overfitting issues are decreased. There are different methods of extracting features. After the extraction of features, the data can be labeled. The method by which the machine takes decisions of labeling data is called a classifier. In other words, a machine uses different classifier algorithms to classify data. Some of the most frequently used classifiers are SVM, RF, LR, DT, NB, KNN, and so on. On the contrary, instead of the step-by-step process like ML, DL forms an entire network inspired by a biological neural network in order to perform the entire process of ML. It uses several layers of nonlinear processing units. The output of a unit is fed as input to the next unit. Throughout the hierarchical structure of data movement, each level transforms the data it receives into more abstract data to be fed to the next level. DL employs different kinds of classifiers including RNN, CNN, Boltzmann machine, autoencoders, and DBN.

Evaluation of the ML/DL detection system to shape its ability to accurately classify brain diseases based on several performance parameters including accuracy (Acy), sensitivity (Sny)/recall, precision specificity (Spy), precision (Prn), AUC and F1 score. Different performance metrics imply different conclusions for a detection model. Although a model can give excellent results in terms of accuracy, it can give very poor results in terms of specificity.

The basic evaluation measure of any classification system is accuracy. It is simply the number of correct guesses given the total number of guesses made. Mathematically, it can be defined as Acy = τp + τN/τp + FP + τN + FN, (1) where τP and τN ​​are true positive and true negative respectively, referring to the correct labeling positive is positive and negative is negative. Negative labeling as positive and vice versa results in false positive (FP) and false negative (FP), respectively.

While accuracy relates to both positive and negative results, the performance of a particular model in terms of positive or negative detection was assessed using sensitivity/recovery and specificity, respectively. Therefore, the sensitivity and specificity were mathematically defined as Sny = τP/τP + FN, Spy = τN/τN + FP. They are also known as true positive rates and true negative rates, respectively. The sensitivity formula implies that it is a measure of successful patient diagnosis. On the other hand, the actual measurement accuracy of the diagnosis, i.e. the proportion of patients diagnosed by a system who are actually affected by the disease. Mathematically, it can be defined as Prn = τP/τP + FP.

On the other hand, the harmonic mean of sensitivity and accuracy is called the F1 score of that model, defined as F1 = 2 × (Sny × Prn/Sny + Prn). Furthermore, the graph of the true-positive versus false-positive ratio is widely used to evaluate the diagnostic power of binary classification systems and is known as the receiver operating characteristic (ROC) curve. The area under the ROC curve (AUC) determines the model’s ability to distinguish between binary options according to the diversity discrimination threshold. Furthermore, MCC is defined as the ratio between specificity and sensitivity. Mathematically, it can be represented as MCC = Spy/Sny. Another evaluation index called Jaccard similarity index (JSI) which can be calculated mathematically is JSI = P/τP + FN + FP

For all AI applications, and ML is no exception, the performance measurement is an essential task. Accuracy, precision, sensitivity, and specificity are metrics widely used to evaluate performance in ML classification tasks. Accuracy and precision reveal a test’s basic reliability, while specificity and sensitivity reveal the likelihood of false negatives (FNs) and false positives (FPs). For these reasons, several works are starting to extend their evaluations by also reporting the Positive Predictive Value (PPV) and Negative Predictive Value (NPV). The Area Under the Curve (AUC) Receiver Operating Characteristic (ROC) curve is one of the most important evaluation metrics to check or visualize the performance of a ML classification problem. It tells how much a model is capable of distinguishing between classes: the higher the AUC, the better the model is at predicting. To make a quantitative evaluation of automatic segmentation results, the frequently used procedure is to determine the overlap with the gold standard that in this field is manual segmentation by an expert radiologist. Generally, Jaccard Coefficient (JC) or Dice Similarity Index (DSI) is used. It ranges from 0 to 1, ranging from no overlap to perfect overlap. For probabilistic segmentation, the validation metric is AUC. Other validation metrics include Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Distance (MAD), and Housdorff Distance (HDD) values. Regarding path planning problems, the most important evaluation metrics reported are the Center of Mass Distance (CMD), Mean Square Distance (MSD), min Square Distance (mSD), and risk score for the trajectory evaluation and time complexity to evaluate the total time of execution for time-constrained applications. For predictive models, the metrics reported are the error rate, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) that can be interpreted as a measure of the ratio between the true and predicted values. As a final remark, it is worth mentioning the efforts spent by researchers to validate their methods, in order to reduce the possibility of human error and handle variations in brain data. To this aim, a crucial role is played by validation methods. Cross-validation methods (kfold, leave-one-out, and leave-one-group out) are still the most valuable approach in this sense. Such methods allow us to better validate ML and DL algorithms, avoiding biases that might be present in a single dataset.

AI technology is showing great potential in supporting the early diagnosis of brain diseases. ML algorithms are capable of analyzing large amounts of multimodal data, which can enable earlier detection of cognitive impairment when interventions are more likely to be effective.

In the future, AI can be used to identify more personalized treatment regimens. The true cost of implementing AI technology is likely to be high. However, as the technology becomes more embedded, the costs involved will decrease and this has the potential to save money on medical services in the end. Combining AI techniques with the growing development of large data sets, biomarker databases, and patient information on electronic medical records will allow for more accurate, efficient results. more efficient and cheaper. Early diagnosis of brain diseases allows much to be done to delay the progression of the disease and keep people independent for longer. A clear diagnosis can also reduce hospitalizations and delay the need for care homes.

Explainable AI is trying to overcome this by using algorithms in a way that humans can understand. Using AI to predict dementia raises a number of legal problems and ethics. Once an individual is diagnosed with brain diseases, there can be serious legal and financial consequences. There is a need to establish trust in AI and consider issues such as accountability, privacy, data governance, cybersecurity, and non-discrimination. This will likely require the development of ethical and legal systems to implement, validate, and control AI in healthcare. It is important to acknowledge that AI is best used when combined with clinical expertise from a multidisciplinary team, and it is not a substitute for them. AI holds promise in promoting effective care and helping to make accurate diagnoses, but its value is as a decision aid rather than as an autonomous agent in the healthcare system. Advancement in AI as an aid in the diagnosis of dementia is happening at a rapid pace and is attracting considerable attention in medical research.

However, this technology has not yet been used routinely in clinical practice and established standards are required to evaluate efficacy and safety prior to implementation. To harness the power of AI technology and make it a broader tool to aid in the diagnosis of dementia, more collaboration between clinicians and AI researchers. This will ultimately allow AI technology to improve early diagnosis, which is important for improving patients’ quality of life.