IndexAbstractIntroductionTechnology and SocietyLiterature ReviewConclusionReferences:AbstractArtificial intelligence or artificial intelligence is interpreted as a discipline of engineering and science. Artificial intelligence involves good computational understanding which is usually known as intelligent behavior or understanding the behavior of an object created by humans. Aristotle tried to give a defined structure to logic (or correct thinking) using syllogisms. Much of the work done in the world of technology is due to previous studies on the functioning of the mind that have finally contributed to recognizing coincident logical thinking. Artificial intelligence systems can also be described as programs that facilitate the operation of computers in such a way as to make people appear intelligent. Alan Turing, a British mathematician is known as the founder of artificial intelligence and computer science today. Alan described the intelligent behavior of a computer as its ability to achieve human-level performance on complex tasks, this later became known as the Turing Test. Towards the middle of the last century, medical researchers discovered the probable significance of the different capacities of intelligence. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay In 1976 Gunn examined the first implementation of artificial intelligence in the field: surgery, he discovered that it is possible to diagnose acute abdominal pain by computer analysis. Over the last 20 years, there has been growth in the medical field regarding artificial intelligence. Today's medicine is experiencing the test of analyzing, obtaining and relating the vast knowledge needed to solve the intricate predicament. The progress of artificial intelligence in the field of medicine is linked to the improvement of artificial intelligence programs that aimed to help doctors in making diagnoses, making decisions about treatment and predicting its effects. They are designed in such a way that they help healthcare workers carry out their tasks in their daily routine, while also supporting the tasks on which the functioning of knowledge and data depend. Systems like these include fuzzy expert system, hybrid intelligent system, evolutionary computing, and ANNs (artificial neural networks). Introduction AI or artificial intelligence is the discipline of computer science capable of inspecting complex medical data. Its ability to identify a useful relationship between collected data can be used in the treatment process, predicting outcomes, diagnosis, and other scientific situations. Procedure: Internet and Medline searches were contrasted using keywords such as “neural networks” and “artificial intelligence.” Cross-referencing various articles leads to multiple references. A general overview of the different artificial intelligence techniques is shown in the following article. Results: The effectiveness of AI techniques has been studied in almost every therapeutic/negotiation discipline. The most commonly used analytical tool has been ANNs (artificial neural networks), while the rest of the AI ​​techniques such as evolutionary computing, hybrid intelligent system and fuzzy expert system have been used in other distinct clinical contexts. Speech: Possesses the ability to be used in all disciplines of medical science. The need for more clinical trials appropriately invented before current techniquesnascent ones that find their exercise in existing clinical contexts. Technology and society The subject of this article is the use of artificial intelligence in the scientific field. There are many known ways to use artificial intelligence in science. Some of them are: The term evolutionary computation is usually used for a set of computational techniques built on a healthy process of evolution that emulate the mechanism of survival and natural selection of the fit in solving real-world problems. “Genetic algorithms” are the most commonly used type of evolutionary computation in medical disciplines. John Holland in 1975 proposed that it is a category of optimization and stochastic search algorithms based on healthy biological evolution. Many non-systematic solutions to problems have been achieved through hard work. This collection of different solutions considered was then progressed from generation to generation, until it finally approached an average result for the problem. The favorable solutions were then brought together to be part of the population while the others were rejected and eliminated. Repeating the same process among the best selected solutions led to repeated improvements in population survival and the production of new solutions. As a result of searches in large and complex spaces, many medical decisions can be made. For example, to check whether the cytology sample is malignant or not, the cytologist will search the area of ​​entire possible cell characteristics for a group of features that allow the cytologist to provide an understandable identification. To study the efficiency in the space provided, the system of natural evolution is exploited by genetic algorithms. Different types of tasks such as prognosis, signal processing, planning, diagnosis, medical imaging and planning are applied. To predict outcomes in critically ill patients, melamoma, lung cancer, and response to warfarin. The principles of genetic algorithms are used. They are also used in computerized investigation of MRI segmentation for brain tumors to calculate how well treatment strategies work, mammographic microcalcification, and for computerized 2D imaging investigation to identify malignant melanomas. Literature Review Using binary threshold functions in 1943 Pitts and McCulloch invented the first ever artificial neuron. The next significant step came when a psychologist named Frank Rosenblatt advanced the “perceptron” in terms of an empirical dummy/model. Their numerous differences compared to the actual “perceptron” network were presented, the most commonly used one being the multilayer feed forward perceptron one. The mentioned networks are composed of layers of neurons, namely an input layer, some or a hidden or intermediate layer and an outer layer, each of which is totally connected to the next layer. Neurons are connected via links and each of the links carries with it a certain numerical weight. With the help of continuous adjustments of link weights, a neural network "learns". A key feature of these ANNs is that they are capable of learning through their exposure to the training environment. The use of the multilayer feed forward perceptron was limited due to the lack of a feasible learning algorithm until a doctoral student, named Paul Werbos, introduced a learning called "backward propagation" in 1974.9 Some others famous network projects contain radial basis function11, Self-Organizing Feature Map.1 and Hopfield networks 10.ANN are used in the analysis of 5 | 5