Global AI-Powered Real-World Evidence (RWE) Solutions Market Size is predicted to reach grow at a 14.6 % CAGR during the forecast period for 2025-2034.
Artificial intelligence (AI)-powered Real-World Evidence (RWE) solutions are those that use AI to gather, examine, and interpret real-world data (RWD) in order to produce insights on the efficacy, safety, and use of medical items in actual clinical settings. Regulatory agencies such as the FDA and the European Medicines Agency (EMA) have implemented frameworks acknowledging the importance of RWE, particularly in capturing treatment performance outside of controlled clinical trials, in response to the growing reliance on RWD to support drug approvals, label expansions, and post-marketing surveillance.
However, using RWE for regulatory reasons is still difficult since data must adhere to strict quality criteria that guarantee it is reproducible, traceable, and statistically sound. RWE's influence is limited, and wider regulatory adoption based on useful, real-world findings is delayed since many pharmaceutical companies still view it as an auxiliary tool rather than a fundamental strategic asset, despite its potential.
The increasing need for AI-powered analytics that can combine genomics with clinical outcomes is being driven by the necessity of genomic data for treating patients according to their unique genetic profiles. The significance of genetic data in population health studies, clinical trial optimization, and medication response prediction is increasing as a result of efforts to connect it with electronic health records (EHRs) and other real-world data sources. Complex genomic information is rapidly being interpreted by advanced AI technologies, such as deep learning and graph-based models, which offer greater insights into disease causes and the effectiveness of treatments. In order to speed up biomarker development, facilitate patient stratification, and improve regulatory submissions, pharmaceutical corporations are now investing heavily in genomics-driven real-world evidence (RWE).
Some of the Major Key Players in the AI-Powered Real-World Evidence (RWE) Solutions Market are:
The AI-powered real-world evidence (RWE) solutions market is segmented into component, data source, therapeutic area, end-user, deployment model, technology, and application. Based on the component, the market is divided into software/platforms, services. Based on the data source, the market is divided into EHR/EMR data, claims data, genomics, wearables/IoT, patient-reported data, and imaging. Based on the therapeutic area, the market is divided into oncology, neurology, cardiology, rare diseases, and immunology. Based on the end-user, the market is divided into pharma/biotech, payers/PBMs, hospitals/IDNs, regulators, and CROs. Based on the deployment model, the market is divided into cloud-based, on-premise, and hybrid. Based on the technology, the market is divided into NLP, computer vision, federated learning, and graph ML. Based on the application, the market is divided into drug development, regulatory submissions, market access, precision medicine.
The complete and longitudinal patient data found in Electronic Health Records (EHRs), such as lab results, diagnoses, prescriptions, procedures, and doctor notes, makes them a fundamental component of AI-powered Real-World Evidence (RWE) systems. These documents are essential for recording clinical outcomes, identifying patient groups, mimicking trial-like conditions, and complying with regulatory frameworks established by organizations such as the FDA and EMA. EHR data is extremely compatible with machine learning and predictive analytics, which increases its usefulness in practical research and decision-making. This is due to the structured elements of EHRs, such as lab panels and ICD codes, as well as developments in natural language processing (NLP), which extract insights from unstructured clinical notes.
NLP has developed into a key component of commercial RWE platforms, generating more precise and scalable real-world insights than more recent technologies like federated learning or graph machine learning. Physician notes, pathology reports, and discharge summaries are just a few examples of the large amount of unstructured healthcare data that makes it difficult to evaluate with conventional techniques. Through the extraction of important insights from these unstructured sources, Natural Language Processing (NLP) facilitates a deeper comprehension and application of clinical data. Since Electronic Health Records (EHRs) are the main source of data for the Real-World Evidence (RWE) market, it is especially important for evaluating clinical narratives inside these systems. Pharmaceutical businesses and regulatory bodies frequently use NLP-based solutions for tasks including outcome tracking, adverse event detection, and cohort identification.
The United States has one of the highest rates of electronic health record (EHR) implementation, generating a vast volume of real-world data (RWD) that serves as a foundation for AI-driven analytics. The U.S. FDA’s Real-World Evidence (RWE) Framework actively supports the use of RWE in regulatory decision-making, fostering confidence and investment from the pharmaceutical and healthcare industries. Additionally, significant funding for AI in healthcare, along with strategic collaborations between technology firms and healthcare organizations, is accelerating the adoption of AI-powered RWE solutions. This growth is further supported by a rapidly expanding healthcare landscape characterized by a growing population, a rising burden of chronic diseases, and a strong focus on enhancing healthcare infrastructure, making North America the leading region in this market.
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Report Attribute |
Specifications |
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Growth Rate CAGR |
CAGR of 14.6% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Mn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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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 Component, Data Source, Therapeutic Area, End-User, Deployment Model, Technology, Application |
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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; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; South East Asia |
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Competitive Landscape |
Aetion, Atropos Health, ConcertAI, Envision Pharma Group, Flatiron Health, Health Compiler, Huma, IQVIA, Ividence.ai, Komodo Health, NVIDIA CLARA, OM1, Okra.ai, Optum, Owkin, Panalgo, Realyze Intelligence, Syntropy, Tempus, Unlearn.AI, Veradigm, Other Prominent Players |
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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-Powered Real-World Evidence (RWE) Solutions Market Snapshot
Chapter 4. Global AI-Powered Real-World Evidence (RWE) Solutions 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. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2024-2034
4.8. Competitive Landscape & Market Share Analysis, By Key Player (2023)
4.9. Use/impact of AI on AI-Powered Real-World Evidence (RWE) Solutions Market Industry Trends
4.10. Global AI-Powered Real-World Evidence (RWE) Solutions Market Penetration & Growth Prospect Mapping (US$ Mn), 2021-2034
Chapter 5. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 1: By Component, Estimates & Trend Analysis
5.1. Market Share by Component, 2024 & 2034
5.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Component:
5.2.1. Software/Platforms
5.2.2. Services
Chapter 6. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 2: By Data Therapeutic Area, Estimates & Trend Analysis
6.1. Market Share by Data Therapeutic Area, 2024 & 2034
6.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Data Therapeutic Area:
6.2.1. EHR/EMR Data
6.2.2. Claims Data
6.2.3. Genomics
6.2.4. Wearables/IoT
6.2.5. Patient-Reported Data
6.2.6. Imaging
Chapter 7. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 3: By End-User Industry, Estimates & Trend Analysis
7.1. Market Share by End-User Industry, 2024 & 2034
7.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following End-User Industry:
7.2.1. Pharma/Biotech
7.2.2. Payers/PBMs
7.2.3. Hospitals/IDNs
7.2.4. Regulators
7.2.5. CROs
Chapter 8. Offline AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 4: By Therapeutic Area, Estimates & Trend Analysis
8.1. Market Share by Therapeutic Area, 2024 & 2034
8.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Therapeutic Area:
8.2.1. Oncology
8.2.2. Neurology
8.2.3. Cardiology
8.2.4. Rare Diseases
8.2.5. Immunology
Chapter 9. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 5: By Deployment Model, Estimates & Trend Analysis
9.1. Market Share by Deployment Model, 2024 & 2034
9.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Deployment Model:
9.2.1. Cloud-Based
9.2.2. On-Premise
9.2.3. Hybrid
Chapter 10. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 6: By Technology, Estimates & Trend Analysis
10.1. Market Share by Technology, 2024 & 2034
10.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Technology:
10.2.1. NLP
10.2.2. Computer Vision
10.2.3. Federated Learning
10.2.4. Graph ML
Chapter 11. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 7: By Application, Estimates & Trend Analysis
11.1. Market Share by Application, 2024 & 2034
11.2. Market Size Revenue (US$ Million) & Forecasts and Trend Analyses, 2021 to 2034 for the following Application:
11.2.1. Drug Development
11.2.2. Regulatory Submissions
11.2.3. Market Access
11.2.4. Precision Medicine
Chapter 12. AI-Powered Real-World Evidence (RWE) Solutions Market Segmentation 8: Regional Estimates & Trend Analysis
12.1. Global AI-Powered Real-World Evidence (RWE) Solutions Market, Regional Snapshot 2024 & 2034
12.2. North America
12.2.1. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
12.2.1.1. US
12.2.1.2. Canada
12.2.2. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
12.2.3. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Data Therapeutic Area, 2021-2034
12.2.4. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
12.2.5. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Therapeutic Area, 2021-2034
12.2.6. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2021-2034
12.2.7. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2021-2034
12.2.8. North America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
12.3. Europe
12.3.1. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
12.3.1.1. Germany
12.3.1.2. U.K.
12.3.1.3. France
12.3.1.4. Italy
12.3.1.5. Spain
12.3.1.6. Rest of Europe
12.3.2. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
12.3.3. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Data Therapeutic Area, 2021-2034
12.3.4. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
12.3.5. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Therapeutic Area, 2021-2034
12.3.6. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2021-2034
12.3.7. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2021-2034
12.3.8. Europe AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
12.4. Asia Pacific
12.4.1. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
12.4.1.1. India
12.4.1.2. China
12.4.1.3. Japan
12.4.1.4. Australia
12.4.1.5. South Korea
12.4.1.6. Hong Kong
12.4.1.7. Southeast Asia
12.4.1.8. Rest of Asia Pacific
12.4.2. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
12.4.3. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Data Therapeutic Area, 2021-2034
12.4.4. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts Therapeutic Area, 2021-2034
12.4.5. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2021-2034
12.4.6. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2021-2034
12.4.7. Asia Pacific AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
12.5. Latin America
12.5.1. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
12.5.1.1. Brazil
12.5.1.2. Mexico
12.5.1.3. Rest of Latin America
12.5.2. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
12.5.3. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Data Therapeutic Area, 2021-2034
12.5.4. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
12.5.5. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Therapeutic Area, 2021-2034
12.5.6. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2021-2034
12.5.7. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2021-2034
12.5.8. Latin America AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
12.6. Middle East & Africa
12.6.1. Middle East & Africa Wind Turbine Rotor Blade Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
12.6.1.1. GCC Countries
12.6.1.2. Israel
12.6.1.3. South Africa
12.6.1.4. Rest of Middle East and Africa
12.6.2. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Component, 2021-2034
12.6.3. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Data Therapeutic Area, 2021-2034
12.6.4. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
12.6.5. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Therapeutic Area, 2021-2034
12.6.6. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Deployment Model, 2021-2034
12.6.7. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Technology, 2021-2034
12.6.8. Middle East & Africa AI-Powered Real-World Evidence (RWE) Solutions Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
Chapter 13. Competitive Landscape
13.1. Major Mergers and Acquisitions/Strategic Alliances
13.2. Company Profiles
13.2.1. Aetion
13.2.1.1. Business Overview
13.2.1.2. Key Component/Service Overview
13.2.1.3. Financial Performance
13.2.1.4. Geographical Presence
13.2.1.5. Recent Developments with Business Strategy
13.2.2. Atropos Health
13.2.3. ConcertAI
13.2.4. Envision Pharma Group
13.2.5. Flatiron Health
13.2.6. Health Compiler
13.2.7. Huma
13.2.8. IQVIA
13.2.9. Ividence.ai
13.2.10. Komodo Health
13.2.11. NVIDIA CLARA
13.2.12. OM1
13.2.13. Okra.ai
13.2.14. Optum
13.2.15. Owkin
13.2.16. Panalgo
13.2.17. Realyze Intelligence
13.2.18. Syntropy
13.2.19. Tempus
13.2.20. Unlearn.AI
13.2.21. Veradigm
13.2.22. Other Prominent Players
Segmentation of the AI-Powered Real-World Evidence (RWE) Solutions Market
Global AI-Powered Real-World Evidence (RWE) Solutions Market - By Component
Global AI-Powered Real-World Evidence (RWE) Solutions Market – By Data Source
Global AI-Powered Real-World Evidence (RWE) Solutions Market – By Therapeutic Area
Global AI-Powered Real-World Evidence (RWE) Solutions Market – By End-User
Global AI-Powered Real-World Evidence (RWE) Solutions Market – By Deployment Model
Global AI-Powered Real-World Evidence (RWE) Solutions Market – By Technology
Global AI-Powered Real-World Evidence (RWE) Solutions Market – By Application
Global AI-Powered Real-World Evidence (RWE) Solutions 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.
To know more about the research methodology used for this study, kindly contact us/click here.
