The AI in Health and Safety Market Size is valued at USD 6.5 billion in 2023 and is predicted to reach USD 28.6 billion by the year 2031 at a 20.9% CAGR during the forecast period for 2024-2031.
AI in health and safety is revolutionizing how risks are managed, and health outcomes are improved. In healthcare, AI aids in early diagnosis, personalized treatments, and efficient patient management. In safety, AI systems enhance workplace safety by predicting risks, monitoring conditions, and automating safety protocols. In the healthcare sector, artificial intelligence (AI) supports drug development, medical imaging analysis, and personalized treatment regimens, leading to more accurate diagnoses and efficient healthcare delivery. Furthermore, AI greatly enhances worker safety in healthcare facilities by managing hospital operations, generating virtual nursing assistants, and offering predictive maintenance. By using AI, health and safety professionals may lower risks, prevent accidents, and raise industry-wide standards for general health and safety.
However, the pandemic also presented certain difficulties, including hiccups in supply chains, postponed R&D projects, and worries about data security and privacy when managing private medical data. Despite these obstacles, the COVID-19 pandemic sped up the digital revolution in the safety and health sectors, highlighting the significance of AI in managing emergencies and enhancing the provision of healthcare.
AI in the health and safety market is segmented by type, application, and end user. Based on type, the market is segmented into machine learning, computer vision, robotics, natural language processing (NLP),and expert systems. By the application segment, the market is categorized into medical image analysis, drug discovery and development, virtual nursing assistants, patient data management, wearable health monitoring, personalized treatment plans, predictive maintenance in healthcare facilities, hospital management, and operations optimization. Based on end-users, the market is segmented into hospitals and clinics, pharmaceutical companies, healthcare IT companies, research institutions, and insurance providers.
In the field of healthcare, Natural discourse Processing (NLP) allows computers to understand, produce, and translate human discourse. It transforms the way healthcare is delivered by revealing insights from unstructured data, expediting processes, empowering patients through chatbots, and improving personalized medication. Healthcare's use of Natural Language Processing (NLP) is transforming the analysis of patient data and risk assessment in Al. NLP facilitates fast and scalable analysis by transforming unstructured text in medical records into structured data. Identifying subtle features sometimes overlooked in organized data enables clinicians to identify patients who are in danger. For example, in January 2023, a major US healthcare payer successfully automated and digitalized their risk adjustment process with IQVIA Inc.'s (US) NLP Risk Adjustment Solution, increasing efficiency by over 25%. They enhanced medical record reviews with NLP.
The healthcare industry is changing as a result of the integration of wearables and smartphones with artificial intelligence (Al). This potent mix is democratizing health data by enabling patients to monitor their vital signs, sleep patterns, activity levels, and moods and take an active role in their well-being. Al algorithms examine the vast amount of personal health data that is produced, allowing for the identification of trends, the forecasting of health concerns, and the customization of treatment regimens. Healthcare is changing as a result of this proactive, data-driven approach, which gives people a better awareness of their health.
With an emphasis on individualized treatment plans, medical image analysis, and predictive maintenance in healthcare facilities, artificial intelligence (AI) technologies are driving improvements in healthcare and safety practices in North America. With an emphasis on virtual nurse assistants, medicine discovery and development, and hospital administration optimization, Europe has also embraced AI applications in health and safety. The Asia Pacific region is expanding quickly, and wearable health monitoring and patient data management powered by AI are becoming more popular.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 6.5 Bn |
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Revenue Forecast In 2031 |
USD 28.6 Bn |
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Growth Rate CAGR |
CAGR of 20.9% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Million and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application, End-User |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
IBM Corporation, Google LLC, Microsoft Corporation, Amazon Web Services, Inc., Intel Corporation, General Electric Company, Siemens Healthineers AG, Medtronic, Inc., Johnson & Johnson Services, Inc., NVIDIA Corporation, Apple Inc., Cerner Corporation, Philips Healthcare, Oracle Corporation, GE Healthcare, Koninklijke Philips N.V., Accenture plc. And Others |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Health and Safety Market Snapshot
Chapter 4. Global AI in Health and Safety Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2023 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:
5.2.1. Machine Learning
5.2.2. Natural Language Processing (NLP)
5.2.3. Computer Vision
5.2.4. Robotics
5.2.5. Expert Systems
Chapter 6. Market Segmentation 2: by End User Estimates & Trend Analysis
6.1. by End User & Market Share, 2023 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by End User:
6.2.1. Hospitals and Clinics
6.2.2. Pharmaceutical Companies
6.2.3. Healthcare IT Companies
6.2.4. Research Institutions
6.2.5. Insurance Providers
Chapter 7. Market Segmentation 3: by Application Estimates & Trend Analysis
7.1. by Application & Market Share, 2023 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:
7.2.1. Medical Image Analysis
7.2.2. Drug Discovery and Development
7.2.3. Virtual Nursing Assistants
7.2.4. Patient Data Management
7.2.5. Wearable Health Monitoring
7.2.6. Personalized Treatment Plans
7.2.7. Predictive Maintenance in Healthcare Facilities
7.2.8. Hospital Management and Operations Optimization
Chapter 8. AI in Health and Safety Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.1.2. North America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
8.1.3. North America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.1.4. North America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.2. Europe
8.2.1. Europe AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.2.2. Europe AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
8.2.3. Europe AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.2.4. Europe AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.3. Asia Pacific
8.3.1. Asia Pacific AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.3.2. Asia Pacific AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
8.3.3. Asia-Pacific AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.3.4. Asia Pacific AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.4. Latin America
8.4.1. Latin America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.4.2. Latin America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
8.4.3. Latin America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.4.4. Latin America AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
8.5. Middle East & Africa
8.5.1. Middle East & Africa AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
8.5.2. Middle East & Africa AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
8.5.3. Middle East & Africa AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
8.5.4. Middle East & Africa AI in Health and Safety Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. IBM Corporation
9.2.2. Google LLC
9.2.3. Microsoft Corporation
9.2.4. Amazon Web Services, Inc.
9.2.5. Intel Corporation
9.2.6. General Electric Company
9.2.7. Siemens Healthineers AG
9.2.8. Medtronic, Inc.
9.2.9. Johnson & Johnson Services, Inc.
9.2.10. NVIDIA Corporation
9.2.11. Apple Inc.
9.2.12. Cerner Corporation
9.2.13. Philips Healthcare
9.2.14. Oracle Corporation
9.2.15. GE Healthcare
9.2.16. Koninklijke Philips N.V.
9.2.17. Accenture plc
9.2.18. Hewlett Packard Enterprise (HPE)
9.2.19. Allscripts Healthcare Solutions, Inc.
9.2.20. Epic Systems Corporation
9.2.21. Cognizant Technology Solutions Corporation
9.2.22. General Vision, Inc.
9.2.23. CloudMedx Inc.
9.2.24. Digital Reasoning Systems, Inc.
9.2.25. Sentrian Pty Ltd.
9.2.26. Other Market Players
AI in Health and Safety Market By Type-
AI in Health and Safety Market By Application-
AI in Health and Safety Market By End-User-
AI in Health and Safety Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
