International Journal of Advancements in Technology
Open Access

Artificial Intelligence in Human Healthcare System

Parisa Zarkeshian^{* *}Correspondence: Parisa Zarkeshian, Department of Computer Science, National Institute of Technology Kurukshetra, Kurukshetra, India, Tel: +919930196175, Fax: +919930196175, Email:

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Descripition

Artificial intelligence can support a variety of patient care and smart healthcare system providers. Artificial intelligence techniques, from machine learning to deep learning, are widely used in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to fully diagnose a disease using artificial intelligence techniques such as ultrasound, magnetic resonance imaging, mammography, genomics, and computer tomography. In addition, artificial intelligence has primarily improved the experience in the clinical and accelerated patient preparation for continued therapeutic rehabilitation. This article describes a comprehensive study based on artificial intelligence technology for diagnosing many diseases such as Alzheimer's disease, cancer, diabetes, chronic heart disease, tuberculosis, stroke, cerebrovascular disease, hypertension, skin and liver disease. An extensive study was conducted, including the medical image datasets used and the classification process for feature extraction and prediction. Select articles published in Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and using systematic reviews and report elements suitable for meta-analysis. Use the data to predict certain types of diseases early. A published intelligence-based approach results are also compared using different quality parameters such as predictor rate, accuracy, sensitivity, specificity, area under the curve, recall based on the study of various articles on disease diagnosis. Healthcare is evolving in front of us with advances in digital healthcare technologies such as artificial intelligence, 3D printing, robotics, and nanotechnology. Digitized healthcare offers many opportunities to reduce human error, improve clinical outcomes, and track data. From machine learning to deep learning, AI techniques play important roles in a variety of health care areas, including new clinical systems, improving patient information and records, and treating a variety of diseases diagnosis. AI technology is also most efficient in identifying the diagnosis of different types of disorders. The presence of computational reasoning as a way to improve medical services provides unprecedented opportunities to restore patient and clinical group results, reduce costs, and more. The model used is not limited to computerization, such as medical service professionals for data creation. AI can also help identify accurate demographics and environmental areas where illness and dangerous behavior are endemic. Analysis is effectively using deep learning classifications in diagnostic approaches to calculate the association between the built environment and obesity rates. AI algorithms need to be trained on representative information to achieve the level of presentation that is essential for adaptive “performance”. Trends such as fees for clearing and directing reality, information gathering through electronic health records and index levels of patient information has created biological data-rich biological systems. This increase in health data suffers from the lack of well-organized mechanisms for integrating and coordinating this data from current silos.

However, a large number of frameworks and principles make summing easier and give you a reasonable amount of data for AI. Despite the information that this is one of the most important expansion areas in biomedical research, the challenges in operational dynamics of AI technology in healthcare systems are immeasurable. The AI community has integrated best practices for execution and assurance by adopting active best practices for principled inclusiveness, software growth, implementation science, and interaction between individual workplace.