The global AI in medical imaging market is projected to reach USD 4.53 Billion by 2029 from USD 1.65 Billion in 2024, at a CAGR of 22.4% during the forecast period. Factors contributing to this significant growth would be increasing government support of AI technologies, increased dependability of radiologists towards AI solutions to reduce loads, and cross-industry collaborations and partnerships. Conversely, the market is posed to suffer from a lack of professional AI workers and inconsistent and ambiguous regulatory frameworks.
Scope of the Report
Years Considered for the Study
2023-2029
Base Year
2023
Forecast Period
2024-2029
Units Considered
Value (USD)
Segments
Component, Application, End User, And Region
Regions covered
North America, Europe, Asia Pacific, Latin America, Middle East & Africa (GCC Countries and RoMEA).
"The services segment is expected to experience the fastest growth in the AI in medical imaging market between 2024 and 2029"
The market has been segmented into software, hardware, and service components. In 2023, the software segment led the market due to its capabilities of optimizing operations, automation of processes, and increased diagnostic precision. However, the services segment is expected to show the highest growth from 2024 to 2029 through rising demand for managed services, integration assistance, and training required for the implementation and optimization of AI solutions in healthcare. These services solve the problems of staff shortage and increased imaging workloads; thus, healthcare providers may enhance operational efficiency and better care for patients.
"The Radiology segment is estimated to account for the largest share of the global AI in medical imaging market in 2023"
The radiology application segment accounts for a large share in the AI in medical imaging market, as there is a strong demand for advanced imaging solutions and the increasing adoption of AI to support radiologists in diagnosing various conditions. The AI technologies are widely applied in radiology to improve diagnostic accuracy, streamline workflow, and reduce the workload of the radiologist, thus yielding faster and more accurate results. The volume of medical imaging procedures is growing, and ongoing advancements in AI technology lead to an expected continuation of its dominance in the market.
Meanwhile, the oncology application segment is also growing pretty fast. The application of AI in oncology is basically an area of early detection, planning of treatment, and the tracking of patient progress that could further analyze medical images, for instance, a CT scan, MRI, and biopsies for symptoms of unusual growths or tumors. This application is being driven rapidly as AI helps to diagnose cancers more accurately and faster, making this aspect of the oncology sector grow in the near future.testing and analysis, is expected to exhibit vigorous growth during the forecast period, driven by growing advancements in AI for precision diagnostics and laboratory automation.
"The Hospitals segment is estimated to account for the largest share of the global AI in medical imaging market in 2023"
In 2023, the hospitals segment is expected to hold the largest share of the global AI in medical imaging market. This is driven by factors such as the growing adoption of minimally invasive surgery (MIS) procedures in hospitals, which enhance the quality of patient care, as well as advancements in imaging technologies that improve workflow efficiency within these healthcare settings.
"North America to dominate the AI in medical imaging market in 2023."
Regarding regional dominance, North America is anticipated to lead the AI in medical imaging market in 2023. This is attributed to the region's advanced healthcare infrastructure, widespread adoption of AI technologies, and significant investments in research and development by key companies and institutions. However, the Asia-Pacific region is projected to experience the highest growth rate (CAGR) between 2024 and 2029, driven by the rising prevalence of cancer, increased utilization of AI in diagnostics, and government efforts to modernize healthcare systems.
Breakdown of supply-side primary interviews, by company type, designation, and region:
By Company Type: Tier 1 (35%), Tier 2 (45%), and Tier 3 (20%)
By Designation: C-level (35%), Director-level (25%), and Others (40%)
By Region: North America (40%), Europe (30%), Asia Pacific (20%), Latin America(5%), and Middle East & Africa (5%)
The prominent players in this market are Microsoft (US), NVIDIA Corporation (US), Merative (US), Intel Corporation (US), Google (US), Siemens Healthineers (Germany), GE HealthCare (US), Digital Diagnostics Inc. (US), Advanced Micro Devices, Inc. (US), InformAI (US), HeartFlow, Inc. (US), Enlitic, Inc. (US), icometrix (Belgium), Aidence (Netherlands), Butterfly Network, Inc. (US), Nano-X Imaging LTD. (Israel), Viz.ai, Inc. (US), Quibim (Spain), Qure.ai (India), Therapixel (France), Aidoc (US), Koninklijke Philips N.V. (Netherlands), Lunit, Inc. (South Korea), EchoNous, Inc. (US), Brainomix (UK).
Research Coverage
The report comprehensively studies the AI in the medical imaging market based on various aspects, including component, application, end user, modality, and region. It analyzes key factors influencing market growth, such as drivers, restraints, opportunities, and challenges. Additionally, the report evaluates the opportunities and challenges faced by stakeholders and provides detailed insights into the competitive landscape for market leaders. It also examines micro-markets, highlighting their growth trends, prospects, and contributions to the overall AI in the medical imaging market. Furthermore, the report forecasts the revenue of market segments across five major regions.
Rationale to Buy the Report
The research report aims to assist both emerging and established companies in understanding the current state of the AI in medical imaging market, enabling them to strategically increase their market share. Companies that acquire the study can utilize the following tactics to enhance their market presence:
The report offers valuable insights on the following aspects:
Key Drivers: Factors such as the influx of big data, a growing number of cross-industry partnerships, the rising adoption of AI solutions to alleviate radiologists' workload, and increasing government initiatives are highlighted as significant growth drivers in the AI in medical imaging market.
Restraints: Challenges include the reluctance among medical practitioners to adopt AI-based technologies, the shortage of skilled AI professionals, and ambiguous regulatory guidelines for medical software, all of which are factors hindering market growth.
Opportunities: The report identifies untapped emerging markets and the increasing focus on developing human-aware AI systems as key opportunities in the market.
Challenges: Budgetary constraints and the presence of unstructured healthcare data are among the challenges that businesses need to overcome to grow in this sector.
Additionally, the report provides the following market insights:
Market Penetration: Detailed analysis of the product portfolios offered by leading players in the AI in medical imaging market.
Product Development/Innovation: Insightful coverage of the innovative products and technologies offered by top players in the AI medical imaging space.
Market Development: Data on profitable developing areas, helping businesses identify opportunities for market expansion.
Market Diversification: Details about recent developments and advancements in the AI in the medical imaging market.
Competitive Assessment: Extensive assessment of the products, growth tactics, revenue projections, and market categories of the top competitors.
This comprehensive research report aims to equip businesses with actionable insights to navigate the evolving landscape of the AI in medical imaging market.
TABLE OF CONTENTS
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED & REGIONAL SCOPE
1.3.2 INCLUSIONS & EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 LIMITATIONS
1.5.1 SCOPE-RELATED LIMITATIONS
1.5.2 METHODOLOGY-RELATED LIMITATIONS
1.6 STAKEHOLDERS
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 List of primary sources
2.1.2.2 Key data from primary sources
2.1.2.3 Key industry insights
2.1.2.4 Breakdown of interviews with experts
2.2 MARKET SIZE ESTIMATION
2.3 MARKET DATA ESTIMATION & TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ASSESSMENT
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 AI IN MEDICAL IMAGING MARKET OVERVIEW
4.2 NORTH AMERICA: AI IN MEDICAL IMAGING MARKET, BY COMPONENT AND COUNTRY (2023)
4.3 AI IN MEDICAL IMAGING MARKET: GEOGRAPHIC GROWTH OPPORTUNITIES
4.4 AI IN MEDICAL IMAGING MARKET: REGIONAL MIX
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Influx of big data with increasing digitization and adoption of information systems
5.2.1.3 Increasing demand for AI-based solutions to reduce work pressure on radiologists
5.2.1.4 Rising government initiatives to support AI-based technologies in healthcare
5.2.1.5 Availability of extensive funding for AI-based startups
5.2.2 RESTRAINTS
5.2.2.1 Reluctance among medical practitioners to adopt AI-based technologies
5.2.2.2 Inadequate AI workforce and ambiguous regulatory guidelines for medical software
5.2.3 OPPORTUNITIES
5.2.3.1 Untapped emerging markets
5.2.3.2 Increasing focus on developing human-aware AI systems
5.2.4 CHALLENGES
5.2.4.1 Budgetary constraints
5.2.4.2 Unstructured healthcare data due to growing digital footprint and technology trends
5.2.4.3 Data privacy concerns amid growing data volume
5.2.4.4 Limited interoperability for AI solutions
5.3 ECOSYSTEM ANALYSIS
5.4 CASE STUDY ANALYSIS
5.4.1 CASE STUDY 1: INTEGRATING AI INTO CLINICAL WORKFLOWS AT LAHEY HOSPITAL & MEDICAL CENTER
5.4.2 CASE STUDY 2: RESOLVING CHALLENGES OF UNDERSTAFFED WORKFORCE AND BACKLOG WITH VEYE LUNG NODULES
5.4.3 CASE STUDY 3: NVIDIA AI ENTERPRISE SOFTWARE AND GPUS HELP IMPROVE PERFORMANCE AND PRECISION OF TUMOR TARGETING
5.4.4 CASE STUDY 4: ZHEJIANG UNIVERSITY AND ZHEJIANG DE IMAGE SOLUTIONS USE INTEL AI SOLUTION TO PROCESS ULTRASOUND
5.4.5 CASE STUDY 5: WAITEMATA DISTRICT HEALTH BOARD PROJECT UTILIZES PRECISION-DRIVEN HEALTH SOLUTIONS
5.5 VALUE CHAIN ANALYSIS
5.5.1 UPSTREAM
5.5.2 MID-STREAM
5.5.3 DOWNSTREAM
5.6 TRADE ANALYSIS
5.6.1 IMPORT DATA
5.6.2 EXPORT DATA
5.7 PORTER'S FIVE FORCES ANALYSIS
5.7.1 THREAT OF NEW ENTRANTS
5.7.2 THREAT OF SUBSTITUTES
5.7.3 BARGAINING POWER OF SUPPLIERS
5.7.4 BARGAINING POWER OF BUYERS
5.7.5 INTENSITY OF COMPETITIVE RIVALRY
5.8 KEY STAKEHOLDERS & BUYING CRITERIA
5.8.1 INFLUENCE OF STAKEHOLDERS
5.8.2 BUYING CRITERIA
5.9 REGULATORY ANALYSIS
5.9.1 REGULATORY LANDSCAPE
5.9.1.1 North America
5.9.1.1.1 Health Insurance Portability and Accountability Act of 1996 (HIPAA)
5.9.1.1.2 Health Information Technology for Economic and Clinical Health Act of 2009 (HITECH)
5.9.1.1.3 Consumer Privacy Protection Act of 2017
5.9.1.1.4 National Cybersecurity Protection Advancement Act of 2015
5.9.1.1.5 Future of Life Institute's Asilomar AI Principles
5.9.1.2 Europe
5.9.1.2.1 European Medical Devices Regulation (EU) 2017/745 and In-vitro Diagnostic Medical Devices Regulation (EU) 2017/746, in combination with General Data Protection Regulation 2016/679
5.9.1.2.2 Artificial Intelligence Act (AI Act)
5.9.1.3 Asia Pacific
5.9.1.3.1 Cybersecurity Law of the People's Republic of China
5.9.1.4 Rest of the World
5.9.1.4.1 Protection of Personal Information Act
5.9.2 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.10 PATENT ANALYSIS
5.10.1 PATENT PUBLICATION TRENDS FOR AI IN MEDICAL IMAGING
5.10.2 JURISDICTION AND TOP APPLICANT ANALYSIS
5.11 TECHNOLOGY ANALYSIS
5.11.1 KEY TECHNOLOGIES
5.11.1.1 Machine learning
5.11.1.2 Deep learning
5.11.2 COMPLEMENTARY TECHNOLOGIES
5.11.2.1 Laboratory automation
5.11.2.2 EHR
5.11.3 ADJACENT TECHNOLOGIES
5.11.3.1 Natural language processing
5.11.3.2 Big data analytics
5.12 PRICING ANALYSIS
5.12.1 INDICATIVE PRICING ANALYSIS FOR AI IN MEDICAL IMAGING HARDWARE, 2021-2023
5.12.2 AVERAGE SELLING PRICE OF AI IN MEDICAL IMAGING HARDWARE, BY KEY PLAYER (2023)