Product Code: 5364
The global artificial intelligence in epidemiology market size is projected to grow substantially over 2022-2030, given the advancement in deep learning, machine learning, predictive analytics, and other innovative technologies.
During drug discovery, a potential candidate can be selected from millions of molecules. A prediction error of at least 1% may lead to the misidentification of over 10,000 molecules. Emphasis on improving the accuracy of machine learning models has therefore risen, which is likely to result in the escalating usage of AI in epidemiology.
With the high burden of chronic diseases, the need for the implementation of effective control measures and the development of effective treatment options has also increased. Based on data from the Centers for Disease Control and Prevention, over 1.7 million people are diagnosed with cancer every year. As a result, the use of advanced technologies including AI in cancer epidemiology applications is expected to witness an upsurge worldwide.
Artificial intelligence in epidemiology industry is segregated in terms of deployment, application, end-use, and region.
Based on deployment, the industry share from the cloud-based segment is set to depict a considerable growth rate through 2030, driven by the rising emphasis on leveraging the benefits of IT infrastructures, mainly during the COVID-19 pandemic.
Healthcare sectors are working to improve privacy, lower expenses, and enhance patient care through remote collaboration and monitoring. These trends will increase the application scope of cloud computing in the healthcare sector to store a huge amount of data and consequently proliferate the adoption of AI technology in epidemiological applications.
With regards to the application spectrum, the infection prediction and forecasting segment is anticipated to register commendable revenue over the estimated timeline. This is credited to the high exposure to new pathogens and the rise of epidemics and pandemics.
The evolution of microbes has also augmented the rate of infectious disease outbreaks globally, which may promote the use of artificial intelligence in epidemiology labs to predict and forecast new infections.
In terms of end-use, the pharmaceutical and biotechnology companies segment is slated to attain a valuation of more than USD 1.3 billion by 2030, given the mounting investment in drug discovery and the escalation of effective drug development initiatives. The adoption of the AI-first approach has also led to increased drug discovery and trial, which is generating opportunities for artificial intelligence in epidemiology industry growth.
From a regional frame of reference, the Middle East and Africa (MEA) region is likely to hold a major share of the artificial intelligence in epidemiology market by 2030. The number of elderly people is growing, which may have a significant impact on healthcare systems due to the increased disease burden. Healthcare infrastructure has also remained robust in countries including the UAE, augmenting the usage of advanced tech including AI for epidemiological surveillance in the healthcare sector.
Table of Contents
Chapter 1 Methodology & Scope
- 1.1 Market definitions
- 1.2 Base estimates and working
- 1.3 Forecast parameters
- 1.4 Data validation
- 1.5 Data sources
- 1.5.1 Secondary
- 1.5.1.1 Paid sources
- 1.5.1.2 Unpaid sources
- 1.5.2 Primary
Chapter 2 Executive Summary
- 2.1 Artificial Intelligence in epidemiology industry 360 degree synopsis, 2017 - 2030
- 2.1.1 Business trends
- 2.1.2 Deployment trends
- 2.1.3 Application trends
- 2.1.4 End-use trends
- 2.1.5 Regional trends
Chapter 3 Artificial Intelligence In Epidemiology Industry Insights
- 3.1 Industry segmentation
- 3.2 Industry landscape, 2017 - 2030 (USD Million)
- 3.3 Industry impact forces
- 3.3.1 Growth drivers
- 3.3.1.1 Rising adoption of artificial intelligence in epidemiology & disease surveillance
- 3.3.1.2 Potential to increase quality of care delivery
- 3.3.1.3 Need to curb incremental healthcare costs
- 3.3.1.4 Advancements in Machine Learning, Deep Learning, and Predictive Analytics
- 3.3.2 Industry pitfalls & challenges
- 3.3.2.1 Security concerns regarding patient data
- 3.3.2.2 Data availability and quality challenges
- 3.4 Growth potential analysis
- 3.4.1 By deployment
- 3.4.2 By application
- 3.4.3 By end-use
- 3.5 COVID- 19 impact analysis
- 3.6 Regulatory landscape
- 3.6.1 U.S.
- 3.6.1.1 Health IT Legislation
- 3.6.1.2 Health Information Technology for Economic and Clinical Health (HITECH) Act
- 3.6.1.3 HIPAA
- 3.6.1.4 Health IT Regulations
- 3.6.1.5 Common Rule
- 3.6.1.6 Federal Trade Commission Act (FTCA)
- 3.6.1.7 FTC Health Breach Notification Rule
- 3.6.2 Europe
- 3.6.3 China
- 3.6.4 Others
- 3.7 Reimbursement scenario
- 3.8 Gap analysis
- 3.9 Start-up scenario
- 3.10 Investment landscape
- 3.11 Policy landscape
- 3.11.1 H-480.940- Augmented Intelligence in Health Care
- 3.11.2 FDA's Digital Health Initiative
- 3.12 Technology landscape
- 3.13 Application potential
- 3.13.1 Public health (Disease Surveillance)
- 3.13.2 Drug Discovery & Development
- 3.14 Porter's Analysis
- 3.15 Competitive landscape, 2021
- 3.15.1 Company matrix analysis, 2021
- 3.16 PESTLE Analysis
Chapter 4 Artificial Intelligence In Epidemiology Market, By Deployment
- 4.1 Key segment trends
- 4.2 On-premise
- 4.2.1 Market size, by region, 2017 - 2030 (USD Million)
- 4.3 Web-based
- 4.3.1 Market size, by region, 2017 - 2030 (USD Million)
- 4.4 Cloud-based
- 4.4.1 Market size, by region, 2017 - 2030 (USD Million)
Chapter 5 Artificial Intelligence In Epidemiology Market, By Application
- 5.1 Key segment trends
- 5.2 Infection Prediction & Forecasting
- 5.2.1 Market size, by region, 2017 - 2030 (USD Million)
- 5.3 Disease & Syndromic Surveillance
- 5.3.1 Market size, by region, 2017 - 2030 (USD Million)
- 5.4 Monitoring Population Health & Incidence/Prevalence
- 5.4.1 Market size, by region, 2017 - 2030 (USD Million)
Chapter 6 Artificial Intelligence In Epidemiology Market, By End-Use
- 6.1 Key segment trends
- 6.2 Government & State Agencies
- 6.2.1 Market size, by region, 2017 - 2030 (USD Million)
- 6.3 Research Labs
- 6.3.1 Market size, by region, 2017 - 2030 (USD Million)
- 6.4 Pharmaceutical & Biotechnology Companies
- 6.4.1 Market size, by region, 2017 - 2030 (USD Million)
- 6.5 Healthcare Providers
- 6.5.1 Market size, by region, 2017 - 2030 (USD Million)
Chapter 7 Artificial Intelligence In Epidemiology Market, By Region
- 7.1 Key regional trends
- 7.2 North America
- 7.2.1 Market size, by country, 2017 - 2030 (USD Million)
- 7.2.2 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.2.3 Market size, by application, 2017 - 2030 (USD Million)
- 7.2.4 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.2.5 U.S.
- 7.2.5.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.2.5.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.2.5.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.2.6 Canada
- 7.2.6.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.2.6.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.2.6.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.3 Europe
- 7.3.1 Market size, by country, 2017 - 2030 (USD Million)
- 7.3.2 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.3.3 Market size, by application, 2017 - 2030 (USD Million)
- 7.3.4 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.3.5 Germany
- 7.3.5.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.3.5.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.3.5.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.3.6 UK
- 7.3.6.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.3.6.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.3.6.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.3.7 France
- 7.3.7.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.3.7.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.3.7.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.3.8 Italy
- 7.3.8.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.3.8.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.3.8.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.3.9 Spain
- 7.3.9.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.3.9.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.3.9.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.4 Asia Pacific
- 7.4.1 Market size, by country, 2017 - 2030 (USD Million)
- 7.4.2 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.4.3 Market size, by application, 2017 - 2030 (USD Million)
- 7.4.4 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.4.5 China
- 7.4.5.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.4.5.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.4.5.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.4.6 Japan
- 7.4.6.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.4.6.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.4.6.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.4.7 India
- 7.4.7.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.4.7.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.4.7.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.4.8 Australia
- 7.4.8.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.4.8.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.4.8.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.4.9 South Korea
- 7.4.9.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.4.9.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.4.9.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.5 Latin America
- 7.5.1 Market size, by country, 2017 - 2030 (USD Million)
- 7.5.2 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.5.3 Market size, by application, 2017 - 2030 (USD Million)
- 7.5.4 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.5.5 Brazil
- 7.5.5.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.5.5.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.5.5.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.5.6 Mexico
- 7.5.6.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.5.6.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.5.6.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.5.7 Argentina
- 7.5.7.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.5.7.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.5.7.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.6 Middle East & Africa
- 7.6.1 Market size, by country, 2017 - 2030 (USD Million)
- 7.6.2 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.6.3 Market size, by application, 2017 - 2030 (USD Million)
- 7.6.4 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.6.5 South Africa
- 7.6.5.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.6.5.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.6.5.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.6.6 Saudi Arabia
- 7.6.6.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.6.6.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.6.6.3 Market size, by end-use, 2017 - 2030 (USD Million)
- 7.6.7 UAE
- 7.6.7.1 Market size, by deployment, 2017 - 2030 (USD Million)
- 7.6.7.2 Market size, by application, 2017 - 2030 (USD Million)
- 7.6.7.3 Market size, by end-use, 2017 - 2030 (USD Million)
Chapter 8 Company Profiles
- 8.1 Competitive dashboard, 2021
- 8.2 Cerner Corporation
- 8.2.1 Business Overview
- 8.2.2 Financial Data
- 8.2.3 Product Landscape
- 8.2.4 Strategic Outlook
- 8.2.5 SWOT Analysis
- 8.3 Cognizant
- 8.3.1 Business Overview
- 8.3.2 Financial Data
- 8.3.3 Product Landscape
- 8.3.4 Strategic Outlook
- 8.3.5 SWOT Analysis
- 8.4 eClinicalWorks, Inc.
- 8.4.1 Business Overview
- 8.4.2 Financial Data
- 8.4.3 Product Landscape
- 8.4.4 Strategic Outlook
- 8.4.5 SWOT Analysis
- 8.5 Epic Systems Corporation
- 8.5.1 Business Overview
- 8.5.2 Financial Data
- 8.5.3 Product Landscape
- 8.5.4 Strategic Outlook
- 8.5.5 SWOT Analysis
- 8.6 Alphabet, Inc.
- 8.6.1 Business Overview
- 8.6.2 Financial Data
- 8.6.3 Product Landscape
- 8.6.4 Strategic Outlook
- 8.6.5 SWOT Analysis
- 8.7 Intel Corporation
- 8.7.1 Business Overview
- 8.7.2 Financial Data
- 8.7.3 Product Landscape
- 8.7.4 Strategic Outlook
- 8.7.5 SWOT Analysis
- 8.8 Microsoft Corporation
- 8.8.1 Business Overview
- 8.8.2 Financial Data
- 8.8.3 Product Landscape
- 8.8.4 Strategic Outlook
- 8.8.5 SWOT Analysis
- 8.9 Meditech
- 8.9.1 Business Overview
- 8.9.2 Financial Data
- 8.9.3 Product Landscape
- 8.9.4 Strategic Outlook
- 8.9.5 SWOT Analysis
- 8.10 Predixion Healthcare (Jvion LLC)
- 8.10.1 Business Overview
- 8.10.2 Financial Data
- 8.10.3 Product Landscape
- 8.10.4 Strategic Outlook
- 8.10.5 SWOT Analysis
- 8.11 Komodo Health
- 8.11.1 Business Overview
- 8.11.2 Financial Data
- 8.11.3 Product Landscape
- 8.11.4 Strategic Outlook
- 8.11.5 SWOT Analysis
- 8.12 Siemens Healthineers
- 8.12.1 Business Overview
- 8.12.2 Financial Data
- 8.12.3 Product Landscape
- 8.12.4 Strategic Outlook
- 8.12.5 SWOT Analysis
- 8.13 Bayer Healthcare
- 8.13.1 Business Overview
- 8.13.2 Financial Data
- 8.13.3 Product Landscape
- 8.13.4 Strategic Outlook
- 8.13.5 SWOT Analysis
- 8.14 Cardiolyse
- 8.14.1 Business Overview
- 8.14.2 Financial Data
- 8.14.3 Product Landscape
- 8.14.4 Strategic Outlook
- 8.14.5 SWOT Analysis
- 8.15 Artificial Intelligence in Medical Epidemiology (AIME)
- 8.15.1 Business Overview
- 8.15.2 Financial Data
- 8.15.3 Product Landscape
- 8.15.4 Strategic Outlook
- 8.15.5 SWOT Analysis
- 8.16 SAS Institute
- 8.16.1 Business Overview
- 8.16.2 Financial Data
- 8.16.3 Product Landscape
- 8.16.4 Strategic Outlook
- 8.16.5 SWOT Analysis