Simply put, artificial intelligence is the ability of computers to mimic the unique intelligence of humans

Simply put, artificial intelligence is the ability of a computer to mimic the unique intelligence of humans. This type of intelligence includes things such as the ability to associate events with specific causes, make generalizations, and learn from experience. As a general public concept, the term is used to describe devices that can provide a cause for a phenomenon, develop strategies, make judgments about situations, and have the ability to learn. However, there has been debate about the level and reliability of this intelligence. Many different theories have been proposed on how to assess machine intelligence. The most famous of these is the Turing test, devised by the British mathematician, computer scientist and cryptographer Alan Turing in 1950. In this test, the evaluator is not sure who gave which answer. Compare the answers given by a computer and a human, and predict which is the machine. If the machine can convince the evaluator with its answer at least 30% of the time, it passes the test.

In 2014, a program called Eugene Gustman passed the test. Types of AI Artificial intelligence is divided into three headings based on technological achievements and future predictions: Narrow AI: Narrow AI, which covers almost all current software is described as AI, which mimics humans within the limited areas in which it was designed Smart and responsive within this framework. Artificial General Intelligence: This type of artificial intelligence has the same intellectual capabilities as humans and is theoretically expected to perform tasks on a par with humans.

The consensus among AI researchers is that this type of AI, also known as human-level AI, must be able to learn and reason, formulate strategies, make plans, communicate using language, and combine all of these abilities to accomplish something. Task. Artificial superintelligence: This type of artificial intelligence is poised to surpass the brightest and most talented human brains, and big names in the tech world such as Stephen Hawking and Elon Musk have proposed grim scenarios for the future associated with its emergence.

Artificial Intelligence Learning Algorithms and Ophthalmology Machine Learning: The term “machine learning” is one of the subcategories of artificial intelligence often used in ophthalmology research, first introduced in 1959 by pioneering artificial intelligence engineer Arthur Samuel. He defines this term as the ability of a machine to learn outcomes that are not explicitly programmed. In machine learning techniques, the goal is to generate an algorithm based on a certain amount of data fed into a computer, which the computer then uses to improve its predictions. The phase in which the device is trained using the input to improve its predictions is the learning phase, which is divided into two types: supervised learning and unsupervised learning.

In supervised learning, labels are assigned to training data as they are fed into the computer, while in unsupervised learning, devices create their own algorithms from unlabeled input. Deep Learning: As machine learning techniques improve and the amount of input increases, this more advanced approach uses multiple layers to generate the output, unlike machine learning, which uses a single layer of operations. With deep neural networks, computers can be trained with larger data volumes and improve themselves in each training cycle to create their own algorithms.

Examples of AI in Medicine Like many other industries, the use of AI in medicine is steadily increasing. Large companies in many medical fields, especially pharmaceuticals and imaging, have invested billions of dollars in this field, and research in artificial intelligence software is also an area of ​​great interest in academia. Although various publications on the application of artificial intelligence in different fields of medicine reveal the wide-ranging uses of these technologies, the number of approved studies is still limited. To cite some notable examples of AI being used in medicine, in 2016 an AI framework from Google called David was shown to be able to identify single nucleotide polymorphisms, the most common genetic Variations, AI applications developed to diagnose tuberculosis by evaluating chest X-rays, evaluating suspicion of malignant melanoma based on photographs of skin lesions, and detecting lymph node metastases in breast cancer by analyzing pathological slides, publications on these represent areas of future use of AI example. This uses radiology as an example. Algorithms developed at Stanford University can diagnose pneumonia more accurately than radiology.

What can artificial intelligence do for ophthalmology? The field of ophthalmology is well suited for AI research, with numerous digital techniques such as color fundus photography, optical coherence tomography, and computerized visual field testing and the vast databases they provide. In addition to this, the increase in global life expectancy has been accompanied by an increase in eye diseases that lead to preventable vision loss. Seek solutions for early diagnosis and treatment of these diseases, especially in hard-to-reach areas. AI applications are being developed for many different eye diseases, especially diabetic retinopathy, age-related macular degeneration, glaucoma, and premature infants Retinopathy, these are the leading causes of vision loss.

Artificial Intelligence and Diabetic Retinopathy DR has generated the greatest interest in the use of artificial intelligence in ophthalmology due to the rapid increase in the number of patients worldwide. The IDx-DR is the first FDA-cleared device to use artificial intelligence software and was also developed for the field. The IDx-DR uses the Topcon NW400 fundus camera to classify patients according to the degree of retinopathy. When choosing a fundus camera, ease of use is a top consideration. The operator who chose to obtain fundus photographs had no previous experience with fundus cameras. Patients were classified into those with mild to advanced DR and those without retinopathy according to the American Academy of Ophthalmology classification (the preferred practice mode for diabetic retinopathy), and patients were recommended for follow-up examinations at 12 months or earlier based on the results. A total of 900 patients were included in the study, and the device was found to have a sensitivity and specificity of 87.4% and 89.5%, respectively. The device started use at the University of Iowa in 2018 after receiving FDA approval. IDx-DR was developed using software using deep learning techniques, and similar studies using fundus cameras and deep learning software are increasingly being conducted. With deep learning applications, software can be developed that contains databases of over 100,000 data points.

There are machine learning methods using fundus photos, OCT machine learning, and deep OCT learning methods. Some of these studies reported sensitivity or specificity rates close to 100%.