Dublin, Jan. 10, 2022 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Epidemiology Market by AI Type, Infrastructure, Deployment Model, and Services 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.
This global AI epidemiology and public health market report provides a comprehensive evaluation of the positive impact that AI technology will produce with respect to healthcare informatics, and public healthcare management, and epidemiology analysis and response. The report assesses the macro factors affecting the market and the resulting need for hardware and software technology used in the public healthcare and epidemiology informatics.
The macro factors include the growth drivers and challenges of the market along with the potential application and usage areas in public health industry verticals. The report also provides the anticipated market value of AI in the public health and epidemiology informatics market globally and regionally. This includes core technology and AI-specific technologies. Market forecasts cover the period of 2022 to 2027.
Select Report Findings:
- The total global value of AI in the epidemiology market will exceed $10 billion by 2027
- Related to vaccine R&D, AI in drug discovery and risk analysis will reach $800 million by 2027
- AI will support various disease-related public health and safety services such as mass notification
- EHR databases will be the foundation, but AI will leverage many sources for disease-related data aggregation
- Epidemiologic predictive models will be dramatically improved through advanced data analytics and various AI tools and techniques
- AI improves the efficiency and effectiveness of transforming healthcare data correlation to meaningful disease insights and information
The Center for Disease Control and Prevention sees epidemiology as the study and analysis of the distribution, patterns, and determinants of health and disease conditions in defined populations. It is a cornerstone of public health and shapes policy decisions and evidence-based practice by identifying risk factors for disease and targets for preventive healthcare.
This includes identification of the factors involved with diseases transmitted by food and water, acquired during travel or recreational activities, bloodborne and sexually transmitted diseases, and nosocomial infections such as hospital-acquired illnesses. Epidemiology is also concerned with the identification of trends and predictive capabilities to prevent diseases.
Artificial Intelligence (AI) will increasingly be relied upon to improve the efficiency and effectiveness of transforming data correlation to meaningful insights and information. For example, machine learning has been used to gather Web search and location data as a means of identifying potential unsafe areas, such as restaurants involved in food-borne illnesses.
The combination of data aggregation from multiple sources with machine learning and advanced analytics will greatly improve the efficacy of epidemiology predictive models. For example, machine learning allows epidemiologists to evaluate as many variables as desired without increasing statistical error, a problem that often arises with multiple testing bias, which is a condition that occurs when each additional test run on the data increases the possibility for error against a hypothetical target result.
An extremely important and high growth area for AI in epidemiology is drug discovery, safety, and risk analysis, which we anticipate will be a $800 million global market by 2027. Other high opportunity areas for AI are disease and syndromic surveillance, infection prediction and forecasting, monitoring population and incidence of disease, and use of AI in Immunization Information Systems. In addition to mapping vaccinations to disease incidence, the IIS will leverage AI to identify the impact of public sentiment analysis and for public safety services such as mass notification.
Key Topics Covered:
Technology and Application Analysis
- Hardware Technology Analysis
- Software Technology Analysis
- AI Technology Analysis
- Enabling Technology Analysis
- Application Analysis
- Industry Use Case Analysis
Company Analysis
- Abbott Informatics
- AdvancedMD
- Agilent Technologies
- Allscripts Healthcare
- Athenahealth
- Autoscribe Informatics
- Cerner Corporation
- Change Healthcare
- Cognizant
- CPSI
- CureMD Healthcare
- e-Mds Inc.
- eClinicalWorks
- Epic Systems
- GE Healthcare
- Graphcore
- Greenway Health
- IBM
- Intel
- InterSystems
- Kareo
- Medhost
- Meditech
- Medtronic
- Micron Technology
- Microsoft
- NextGen Healthcare
- Nvidia
- Optum Inc.
- Philips Healthcare
Market Analysis and Forecasts 2022 - 2027
Conclusions and Recommendations
For more information about this report visit https://www.researchandmarkets.com/r/raknj
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