USE ARTIFICIAL INTELLIGENCE IN DIAGNOSIS PATIENTS IN MEDICINE
Keywords:
Artificial intelligence, science in medicine, new equipment, diagnostic processes.Abstract
Man-made brainpower can possibly change how patients are analyzed in the clinical field. As man-made intelligence and AI innovations keep on propelling, there are expanding potential chances to use these apparatuses to further develop finding exactness and productivity. Man-made intelligence frameworks have shown a capacity to examine huge measures of clinical information, including pictures, test results, and patient accounts, to distinguish examples and make symptomatic proposals. This logical power can assist with tending to a portion of the difficulties looked in conventional finding techniques.
References
Connell GCO, Chantler PD, Barr TL. Stroke-associated pattern of gene expression previously identified by machine-learning is diagnostically robust in an independent patient population. Genomics Data. 2017; 14:47–52. doi: 10.1016/j.gdata.2017.08.006.
Dabowsa N, Amaitik N, Maatuk A, Shadi A (2017) A hybrid intelligent system for skin disease diagnosis. In: Conference on engineering and technology, pp 1–6. 10.1109/ICEngTechnol.2017.8308157
Fukuda M, Inamoto K, Shibata N, Ariji Y, Kutsana S. Evaluation of an artificial system for detecting vertical root fracture on panoramic radiography. Oral Radiol. 2019; 36:1–7.
Gao XW, James-Reynolds C, Currie E. Analysis of Alzheimer severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Healthc Technol. 2019 doi: 10.1016/j.neucom.2018.12.086.
Haq AU, Li JP, Memon MH, Nazir S, Sun R. A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mob Inf Syst. 2018; 8:1–21. doi: 10.1155/2018/3860146.