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.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 6.5 Bn |
| Revenue Forecast In 2031 | USD 28.6 Bn |
| Growth Rate CAGR | CAGR of 20.9% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Million and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type, Application, End-User |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| 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 |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
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-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.