Artificial Intelligence used in neurology and disease diagnosis is machine learning medical information seeking to improve patient care by gaining new insights from data obtained by a particular patient’s health as well as the shared intelligence of many patients. It has become more associated with assistance and efficiency within the medical environment, rather than being viewed of suspiciously, instead it is the second pair of eyes that never sleep while providing dependable support to medical staff and facilities (Long,M.2020). The brain is considered as being the most essential organ in the human body. It supervises and controls our actions and reactions, allows us to think and feel, and gives us memories and feelings. As a result, without adequate research, supporting devices such as MI may cause potential harm to patients, the use of the above-mentioned technologies without the correct information may result in incorrect diagnosis and treatment plans, putting patients at greater risk of harm (Muehlematter, et al2021). According to signify, an independent provider of market information and advising to the global healthcare technology industry, the global market for machine learning in medical imaging, which includes software for early diagnosis, measurement, prediction, and diagnosis, is destined for rapid growth and is expected to exceed $2 billion by 2023. The eyes and nervous systems are all part of the of the brain and when studies are carried out and is known as neuropathology. The study of the nervous system's growth, structure, and function, and what it does is known as neuroscience. Diseases affecting the peripheral and central nervous system are referred to as neurological disorders common symptoms include tissue weakness, paralysis, seizures, distress, poor coordination, and disorientation. There are over 600 nervous system diseases, motor neurone disease, and Alzheimer's disease amongst many more Neuroscientists study the brain and how it affects behaviour and cognitive abilities and all the above diagnosis. Neuroscience is involved not only with the usual development of the nervous system, but also with what tends to happen to the human nervous system when people suffer from certain neurological diseases (Harris, 2018). Researchers have recommended again for scientific proof that a computer-aided diagnosis (CAD) system conditioned to use a large amount of patient information such as signals, and pictures, and predicated just on information collected from the technology detection and artificial intelligence (AI) device learning techniques in an automatic direction, can help neurologists, neurosurgeons, radiologists, and other health personnel in creating better medical judgment. Research in this area has grown tremendously in recent years over the last couple of years. Magnetic resonance imaging (MRI) collects images of the brain and can assist in the diagnosis of situations such as brain tumors or if existing damage to the brain has occurred because of strokes(Raghavendra, et al, 2019). Image Transformation is one the most common Machine Learning -CAD used for neurological diagnosis, in general, image transformation begins with the removal of unnecessary data, followed by the image retrieval from the processed image. This step facilitates all data collected to help with diagnosis three image transformation machines that is use this technique are FMRI, MRI and PET scan. FMRI is machine learning showing signals to the machine, while you are in the scanner the machine is showing a movie and the machine is guessing what you are watching and from the way your brain activity is reacting the machine can guess what you are watching, it is also believed that this has also been trialled to a person sleeping to decode the individuals’ dreams. Giving all the above information researchers can agree that with Artificial intelligence been used within neurology needs to have extensive research completed and trailed before been implemented to help medical experts give diagnosis to patients as some diagnosis can be life threatening and need the correct medical plan in place for treating a patient’s diagnosis(Raghavendra, et al 2019). Bibliography
Muehlematter, U.J., Daniore, P. and Vokinger, K.N. (2021). Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. The Lancet Digital Health, [online] 3(3), pp.e195–e203. Available at: https://www.sciencedirect.com/science/article/pii/S2589750020302922. [Accessed the 18th February 2022] Long, M. (2020). Artificial Intelligence in Medical Diagnosis | Healthcare Insights. [online] Aidoc. Available at: https://www.aidoc.com/blog/artificial-intelligence-medical-diagnosis/. [Accessed 18th February 2022] Harris, S. (2018). AI in Medical Imaging to Top $2 Billion by 2023. [online] Signify Research. Available at: https://www.signifyresearch.net/medical-imaging/ai-medical-imaging-top-2-billion-2023/ [Accessed 18th February 2022]. Raghavendra, U., Acharya, U.R. and Adeli, H. (2019). Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders. European Neurology, [online] 82(1-3), pp.41–64. Available at: https://www.karger.com/Article/FullText/504292 [Accessed 18th February. 2022]. Image References Daleena Health Care Neurology, [Available at], https://daleenahealthcare.com/en/neurology/. Bansal, P. (2020). AI and Neuroscience: A Remarkable Relationship, [Available at], https://medium.com/techtalkers/ai-and-neuroscience-a-remarkable-relationship-fddfbd860ae3. B. Cesar (2015). Cognitive and White Matter Tract Differences in MS and Diffuse Neuropsychiatric Systemic Lupus Erythematosus,[Available at], http://www.ajnr.org/content/36/10/1874/F3.
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