Short Communication, Gigs Vol: 9 Issue: 5

Geo Artificial System Applications In Health And Healthcare System

 Gongwen Wang

 Department of Artificial intelligent, whuhan University, china. 

*Corresponding Author: Gongwen Wang, Department of Artificial intelligent, 
whuhan University, china. E-mail: gww@cugb.edu.cn

Received: April 30, 2021 Accepted: May 14, 2021 Published: May 21 ,2021

Citation: Wang G. (2021) GEO ARTIFICIAL SYSTEM APPLICATIONS IN HEALTH AND HEALTHCARE SYSTEM. Geo infor Geo stat: An Overview 9(5).300

Abstract

Keywords: Geoinformatics, geodynamics

Introduction

The moulding together geographic/geographic information systems (GIS) dimension creates Geo Artificial intelligent. An emerging role for GeoAI in health and healthcare, as location is an integral a part of both population and individual health. Which provides text of GeoAI technologies like methods, tools and software and their current and potential applications in several disciplines within public health, precision medicine, and Internet provided smart healthy cities. Artificial intelligence (AI), like methods in machine learning, has been increasingly utilized in health and healthcare, particularly with the increase of highperformance and cloud computing capabilities [1,2] Health intelligence refers to the precise application of AI and data science methods and tools to supply accurate, efficient, and productive insights into healthcare and medicine. Health intelligence applications have included social media analytics for syndromic surveillance, predictive modelling to spot populations at high risk for disease mobile health for health care delivery and medical imaging interpretation. [3,4,5,6,7] AI to enhance health at the population level and at the individual level . Population-level applications include those aimed toward promoting public health like through the disciplines of environmental health, epidemiology, genetics, social and behavioural sciences, and infectious diseases. In contrast, individual-level applications are often geared towards precision medicine, or disease management that considers individual variability in genetics, environment, and lifestyle [8,9]. target scale of the population or individual, location or place is a crucial consideration in health intelligence because it can play a big role in health. The locations during which we live, work, and spend our time are related to factors, including but not limited to the built environment, environmental exposures, and social determinants, which will impact our health. Incorporating location-based information can allow us to raised understand risk factors for disease and identify novel targets for prevention efforts.Spatial science offers tools and technologies that enable us to know , analyse, and visualize real-world phenomena consistent with their locations. GeoAI represents a focused domain within health intelligence that comes with location to derive aand individual level is that the use of novel sources of spatial big data, like social media, electronic health records, satellite remote sensing, and private sensors, to advance the science of public health (especially within the context of ‘smart healthy cities’) and potentially precision medicine, creating new opportunities to more comprehensively answer questions typically tackled in these fields also as unique opportunities. generate and consume large amounts of massive health and environmental data. GeoAI can play a key role in making sense of those data through intelligent, location-based big data analytics. GeoAI tools and applications aim to utilise all of those methods as relevant to get valuable information and knowledge from spatial big data for specific analytical needs [10]. Reference 1. Topol EJ. (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med.25(1):44–56 2. VoPham T, Hart JE, Laden F, Chiang YY. (2018).Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology. Environ Health.17(1):40 3. Shaban-Nejad A, Michalowski M, Buckeridge DL.(2018).Health intelligence: how artificial intelligence transforms population and personalized health. London: Nature Publishing Group. 4. Ã?Â?erban O, Thapen N, Maginnis B, Hankin C, Foot V. (2018).Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification. Inf Process Manag. 56:1166–84 5. Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, et al (2018) Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 1(1):18 6. Istepanian RSH, Al-Anzi T. (2018).m-Health 2.0: new perspectives on mobile health, machine learning and big data analytics. Methods. 151:34–40 7. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ,et al. (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin.69:127–57 8. Davis MM, Shanley TP.(2017).The missing-omes: proposing social and environmental nomenclature in precision medicine. Clin Transl Sci.10(2):64–6. 9. Hu Y, Gao S, Newsam S, Lunga D. GeoAI (2018). workshop report the 2nd ACM SIGSPATIAL international workshop on GeoAI: AI for geographic knowledge discovery seattle, WA, SIGSPATIAL special,10(3), 1–16. 10. Maged N. Kamel Boulos, Guochao Peng & Trang VoPham (2019) An overview of GeoAI applications in health and he