의료 분야 인공지능(AI) 시장 : 구성요소별, 기술 유형별, 전개 방식별, 응용 분야별, 최종사용자별, 질환 유형별 - 세계 예측(2025-2030년)

The Artificial Intelligence in Medicine Market was valued at USD 12.64 billion in 2024 and is projected to grow to USD 15.62 billion in 2025, with a CAGR of 24.37%, reaching USD 46.81 billion by 2030.

Artificial Intelligence is rapidly transforming the landscape of modern medicine by introducing innovative methods to diagnose, treat, and manage diseases with unparalleled precision and speed. Medical professionals and decision-makers are now leveraging powerful algorithms and advanced computing systems that not only analyze complex medical data but also provide actionable insights which help in enhancing patient outcomes. This report provides an in-depth look into the multi-faceted evolution of AI in healthcare, underscoring the major drivers behind its adoption and the transformative shifts propelling the industry forward.

The convergence of healthcare needs with breakthrough technological applications is creating an environment where both clinical and operational efficiencies can be significantly improved. As each stakeholder moves towards embracing digital transformation, there is a growing emphasis on bridging gaps between traditional practices and future-forward technological innovations. In this dynamic ecosystem, the integration of AI is not an isolated phenomenon but a systemic change that touches on all levels of the healthcare value chain.

Through comprehensive research and rigorous analysis, this report aims to serve as a valuable resource for industry professionals by highlighting key trends, segmentation insights, regional variations, and the leading companies spearheading these changes. As the field continues to mature, stakeholders must develop a nuanced understanding of these complex interactions to stay competitive and harness the full potential of AI-driven healthcare innovations.

Transformative Shifts Reshaping the AI in Medicine Landscape

The healthcare industry is undergoing a seismic transformation driven by the pervasive adoption of Artificial Intelligence. What was once seen as a futuristic concept is now a crucial component of everyday healthcare, reshaping every facet from diagnostics to treatment. The industry landscape has shifted as research, investment, and policy revisions place AI technologies at the forefront of medical innovation.

Notably, the transformation encompasses both service-oriented and software-driven elements. On one hand, a detailed study of the market with respect to components reveals a dual focus: services and software. Within services, consulting and integration & deployment services are being optimized to better adapt to the dynamic needs of healthcare institutions. On the other hand, software plays a pivotal role, with applications software and system software providing the technical backbone required for complex analyses and decision-making processes.

In addition to component segmentation, technological evolution underlines a critical driver of transformation. Technologies such as computer vision, machine learning, natural language processing, and robotics are not only redefining how medical data is interpreted but are also facilitating real-time decision-making and personalized care. This holistic approach is further complemented by strategic deployment modes that include both cloud-based and on-premise solutions, ensuring that healthcare organizations can optimize infrastructure costs while maintaining robust data security and high performance.

Application areas of AI in medicine have expanded to include diagnostics, drug discovery, and treatment methodologies. The diagnostic domain itself has seen advancements through medical imaging and pathology detection innovations, thereby enabling more accurate and early detection of diseases. In parallel, efforts in drug discovery and therapeutic interventions are being accelerated by AI-powered techniques that streamline research and clinical trials.

This evolution extends to a refined understanding of end-user sectors such as hospitals, clinics, pharmaceutical companies, and research institutes. Each of these groups benefits from tailored AI applications, ensuring that the deployment of technology is not only effective but also specifically aligned with user needs. Moreover, a detailed exploration of disease types including cardiology, dermatology, gastroenterology, neurology, obstetrics & gynecology, oncology, ophthalmology, orthopedics, pediatrics, and urology has further refined market strategies. These category-based insights enable stakeholders to target specific areas for growth and operational enhancement.

Overall, the transformative shifts observed in AI within the medical realm signal a robust reconfiguration of traditional paradigms. They offer a glimpse into a future where technology-enhanced healthcare isn't just a possibility but a standard, offering more precise, efficient, and patient-centric care.

In-Depth Insight into Market Segmentation

A comprehensive understanding of market segmentation is essential to grasp the multifaceted nature of AI's integration into healthcare. The segmentation framework forms the backbone of market analysis by differentiating between the intrinsic and operational domains. The first segmentation, based on component, takes an analytical deep dive into services and software. The services aspect further branches into consultancy-led strategies and integration plus deployment solutions that ensure seamless adoption of AI technology. Whereas the software segment is meticulously examined by categorizing it into applications software that caters to end-user interfaces and system software which handles critical back-end functions.

The second segmentation layer focuses on technology type, dissecting the market through the lenses of computer vision, machine learning, natural language processing, and robotics. Each technology is evaluated on its potential to transform specific segments of medical procedures, from interpreting complex diagnostic images to formulating precise treatment plans based on vast datasets. This holistic approach enables market participants to identify pockets of opportunity that align technological capabilities with healthcare needs.

Further granularity is achieved by examining the deployment mode. In evaluating cloud-based against on-premise solutions, the analysis takes into account scalability, security, and cost-effectiveness, thereby equipping organizations with the choices that best suit their operational framework.

The segmentation analysis extends into application areas where distinct groups such as diagnostics, drug discovery, and treatment are scrutinized. It is interesting to note that within the diagnostic sphere, there is a pronounced focus on sub-segments like medical imaging and pathology detection, which have emerged as key drivers of clinical decision-making.

Equally important is the detailed study of end-users, which includes a diverse set encompassing healthcare providers, pharmaceutical companies, and research institutes alongside academic centers. Among healthcare providers, both clinics and hospitals are given specific attention, reflecting the varied scale and scope of AI implementation across different clinical settings.

The final segmentation dimension considers disease types, offering insights across a spectrum that ranges from cardiology through dermatology, gastroenterology, neurology, obstetrics & gynecology, oncology, ophthalmology, orthopedics, pediatrics, and urology. This categorization provides an exhaustive view of the clinical areas that stand to benefit most from technological interventions, allowing stakeholders to align research and investment strategies with actual disease prevalence and treatment advancements.

In synthesizing these segmentation insights, it becomes evident that the market is not monolithic but rather a complex tapestry where each component, technology, deployment mode, application area, end-user, and disease type interplays to form a comprehensive understanding of AI's current and future potential in medicine. The depth and breadth of this segmentation offer a roadmap for targeted strategies and timely interventions, allowing industry leaders to make decisions that reflect both immediate needs and long-term trends in the healthcare landscape.

Based on Component, market is studied across Services and Software. The Services is further studied across Consulting Services and Integration & Deployment Services. The Software is further studied across Applications Software and System Software.

Based on Technology Type, market is studied across Computer Vision, Machine Learning, Natural Language Processing, and Robotics.

Based on Deployment Mode, market is studied across Cloud-Based and On-Premise.

Based on Application Areas, market is studied across Diagnostics, Drug Discovery, and Treatment. The Diagnostics is further studied across Medical Imaging and Pathology Detection.

Based on End-User, market is studied across Healthcare Providers, Pharmaceutical Companies, and Research Institutes & Academic Centers. The Healthcare Providers is further studied across Clinics and Hospitals.

Based on Disease Type, market is studied across Cardiology, Dermatology, Gastroenterology, Neurology, Obstetrics & Gynecology, Oncology, Ophthalmology, Orthopedics, Pediatrics, and Urology.

Key Regional Insights Driving Global Trends

Regional trends play a critical role in shaping the overall market dynamics for AI in medicine. Different regions display varied levels of adoption, technological infrastructure, and regulatory environments, each contributing uniquely to the market's evolution. In the Americas, there is a high concentration of healthcare innovation driven by robust funding ecosystems, advanced research facilities, and early technology adoption. This region's ecosystem supports rapid integration of AI-driven solutions into clinical workflows and operational strategies, leading to improvements in patient outcomes and cost efficiencies.

Across Europe, the Middle East, and Africa, regulatory frameworks and public-private partnerships serve as catalysts for technological growth. Investments in technology, bolstered by localized research initiatives, have fostered an environment conducive to both incremental improvements in existing systems and breakthrough innovations. This area emphasizes balanced growth where stringent regulatory measures ensure patient safety while promoting industry-wide advancements in AI applications.

In the Asia-Pacific region, rapid digital transformation is fueled by increasing healthcare demands and a growing population whose needs drive innovative solutions. The region benefits from supportive government policies that encourage technology transfer and collaborative research. These strategies have led to significant advancements in personalized medicine, efficient healthcare delivery, and the overall expansion of AI's footprint in various segments of the healthcare market.

The diverse regional nuances reflect how different factors such as policy frameworks, economic dynamics, and cultural considerations shape market strategies. Stakeholders who understand these regional insights can better navigate the complexities of international markets while tailoring their approaches to maximize local advantages. By leveraging regional strengths and addressing unique challenges, industry leaders are positioned to capitalize on growth opportunities and steer the evolution of AI in medicine on a global scale.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Comprehensive Analysis of Leading Industry Players

The competitive landscape of AI in medicine is marked by a broad spectrum of companies that are spearheading innovation and market transformation. Various renowned organizations are actively shaping the future of healthcare through breakthrough research, strategic partnerships, and a relentless focus on delivering value. Notable players include Aidoc Medical Ltd., Allscripts Healthcare Solutions, Inc., and BenevolentAI Limited, each contributing robust expertise in technology integration and clinical application. Additional influential companies such as Butterfly Network, Inc. and CloudMedx Inc. are driving advancements in diagnostics and real-time analytics while Enlitic, Inc. and Epic Systems Corporation continue to refine their offerings, ensuring that complex medical data translates into actionable insights.

Further stirring the industry are companies like Exscientia plc and Freenome Holdings, Inc., which have made significant inroads in drug discovery and cancer diagnostics respectively. Solid examples of this trend include GE Healthcare and Google LLC by Alphabet Inc., both harnessing vast swathes of data to optimize medical imaging and operational efficiency. HeartFlow, Inc. and IBM Corporation have also been pivotal in integrating AI technologies into routine clinical analyses, ensuring that the healthcare ecosystem becomes more predictive and responsive.

Other key contributors include Insilico Medicine, Inc., Intel Corporation, and Koninklijke Philips N.V., which are recognized for their innovative approaches to healthcare challenges. Medtronic plc and NVIDIA Corporation are advancing the frontier of medical device innovation with AI-powered capabilities, while companies such as Owkin, Inc. and PathAI, Inc. stand out for their cutting-edge research in pathology and diagnostics. Qventus, Inc. alongside Recursion Pharmaceuticals, Inc. are redefining operational efficiencies and drug formulation techniques, further complemented by the advancements of Siemens Healthineers AG and Tempus Labs, Inc.

Prominent players such as Viz.ai, Inc. and Zebra Medical Vision Ltd. illustrate a continued drive toward making AI accessible in everyday clinical practice. The diverse portfolios and proven track records of these companies underscore not only the technological advances within the medical field but also the importance of strategic positioning and continuous innovation. Their collective efforts are instrumental in bridging the gap between emerging research trends and real-world application, ensuring that AI continues to elevate standards of care across the globe.

The report delves into recent significant developments in the Artificial Intelligence in Medicine Market, highlighting leading vendors and their innovative profiles. These include Aidoc Medical Ltd., Allscripts Healthcare Solutions, Inc., BenevolentAI Limited, Butterfly Network, Inc., CloudMedx Inc., Enlitic, Inc., Epic Systems Corporation, Exscientia plc, Freenome Holdings, Inc., GE Healthcare, Google LLC By Alphabet Inc., HeartFlow, Inc., IBM Corporation, Insilico Medicine, Inc., Intel Corporation, Koninklijke Philips N.V., Medtronic plc, NVIDIA Corporation, Owkin, Inc., PathAI, Inc., Qventus, Inc., Recursion Pharmaceuticals, Inc., Siemens Healthineers AG, Tempus Labs, Inc., Viz.ai, Inc., and Zebra Medical Vision Ltd.. Actionable Recommendations for Industry Leaders

In light of the evolving landscape and multifaceted segmentation detailed above, industry leaders are advised to take several specific steps to secure a competitive advantage and drive sustainable growth. An immediate priority should be the strategic adoption of flexible technology solutions that seamlessly bridge cloud-based and on-premise infrastructures, ensuring both scalability and security. Leaders must explore integrating advanced analytical tools that harness the power of machine learning, computer vision, natural language processing, and robotics. This integrative approach can streamline complex operations and improve clinical outcomes without a steep learning curve or disruptive process changes.

Moreover, it is crucial to align market entry strategies with a strong understanding of regional differences. Companies operating across the Americas, Europe, the Middle East, Africa, and Asia-Pacific should tailor their tactics to address specific regulatory frameworks, healthcare funding models, and patient demographics unique to each region. Establishing localized research initiatives and forging robust collaborations with local healthcare providers and academic institutions can also catalyze innovation and facilitate the easier adoption of AI-driven processes.

Investment in specialized segmentation such as consulting, integration, and advanced system software should be prioritized to maximize operational efficiencies. Industry players would benefit from developing dedicated teams focused on monitoring emerging trends in diagnostics, drug discovery, and treatment, ensuring that strategies remain aligned with the latest scientific and technological breakthroughs.

Concurrently, fostering partnerships with leading technology vendors and research institutions will enable an agile response to rapidly evolving market dynamics. It is advisable to allocate resources toward continuous training programs and workshops to ensure that teams are well-versed in leveraging state-of-the-art AI applications effectively.

Adopting these recommendations, while maintaining a keen focus on both immediate and long-term objectives, will empower industry leaders to not only anticipate future market shifts but also act decisively in harnessing the unprecedented potential of AI to transform healthcare delivery.

Conclusion: Embracing a Future Driven by AI Innovation

In summary, the penetration of Artificial Intelligence into the realm of medicine signifies a paradigm shift that transcends traditional clinical methodologies. Every segment of the market-from service and software components to advanced technology types and nuanced deployment models-demonstrates that AI is not simply an add-on but a crucial catalyst for a complete reengineering of healthcare delivery. Detailed segmentation insights reveal a multi-dimensional space, where innovations in diagnostics, drug discovery, and therapeutic solutions are tailored to meet diverse needs. Furthermore, regional variations and the strategic positioning of leading companies collectively offer a roadmap for sustainable market growth.

Ultimately, the confluence of these trends, dynamics, and actionable recommendations paints a clear picture of a future where technology and medicine converge to offer transformative results. As stakeholders continue to invest in and integrate AI, the trajectory of medical innovation will be characterized by improved patient care, reduced operational friction, and a new era of data-driven clinical excellence. By embracing these shifts, the medical community is poised to lead the charge towards an ecosystem that is both efficient and adaptive to the ever-changing landscape of healthcare.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Global aging demographics are creating a demand for AI solutions to support elderly care and management
    • 5.1.1.2. Increased demand for personalized medicine fuels the growth of AI technologies in healthcare
  • 5.1.2. Restraints
    • 5.1.2.1. High initial implementation costs and ROI concerns for AI technologies in medicine
  • 5.1.3. Opportunities
    • 5.1.3.1. Rising partnerships between technology companies and healthcare providers for developent and adoption of AI in medicine
    • 5.1.3.2. Integration of AI in robotic surgery to enhance precision, reduce recovery times, and minimize surgical risks
  • 5.1.4. Challenges
    • 5.1.4.1. Shortage of skilled professionals trained in artificial intelligence for healthcare
• 5.2. Market Segmentation Analysis
  • 5.2.1. Technology Type: Adoption of machine leaning technology in medicine for improving patient care outcomes through data-driven insights
  • 5.2.2. End-User: Usage of artificial inteligence in hospitals for diagnostic imaging, and predictive analytics
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Artificial Intelligence in Medicine Market, by Component

• 6.1. Introduction
• 6.2. Services
  • 6.2.1. Consulting Services
  • 6.2.2. Integration & Deployment Services
• 6.3. Software
  • 6.3.1. Applications Software
  • 6.3.2. System Software

7. Artificial Intelligence in Medicine Market, by Technology Type

• 7.1. Introduction
• 7.2. Computer Vision
• 7.3. Machine Learning
• 7.4. Natural Language Processing
• 7.5. Robotics

8. Artificial Intelligence in Medicine Market, by Deployment Mode

• 8.1. Introduction
• 8.2. Cloud-Based
• 8.3. On-Premise

9. Artificial Intelligence in Medicine Market, by Application Areas

• 9.1. Introduction
• 9.2. Diagnostics
  • 9.2.1. Medical Imaging
  • 9.2.2. Pathology Detection
• 9.3. Drug Discovery
• 9.4. Treatment

10. Artificial Intelligence in Medicine Market, by End-User

• 10.1. Introduction
• 10.2. Healthcare Providers
  • 10.2.1. Clinics
  • 10.2.2. Hospitals
• 10.3. Pharmaceutical Companies
• 10.4. Research Institutes & Academic Centers

11. Artificial Intelligence in Medicine Market, by Disease Type

• 11.1. Introduction
• 11.2. Cardiology
• 11.3. Dermatology
• 11.4. Gastroenterology
• 11.5. Neurology
• 11.6. Obstetrics & Gynecology
• 11.7. Oncology
• 11.8. Ophthalmology
• 11.9. Orthopedics
• 11.10. Pediatrics
• 11.11. Urology

12. Americas Artificial Intelligence in Medicine Market

• 12.1. Introduction
• 12.2. Argentina
• 12.3. Brazil
• 12.4. Canada
• 12.5. Mexico
• 12.6. United States

13. Asia-Pacific Artificial Intelligence in Medicine Market

• 13.1. Introduction
• 13.2. Australia
• 13.3. China
• 13.4. India
• 13.5. Indonesia
• 13.6. Japan
• 13.7. Malaysia
• 13.8. Philippines
• 13.9. Singapore
• 13.10. South Korea
• 13.11. Taiwan
• 13.12. Thailand
• 13.13. Vietnam

14. Europe, Middle East & Africa Artificial Intelligence in Medicine Market

• 14.1. Introduction
• 14.2. Denmark
• 14.3. Egypt
• 14.4. Finland
• 14.5. France
• 14.6. Germany
• 14.7. Israel
• 14.8. Italy
• 14.9. Netherlands
• 14.10. Nigeria
• 14.11. Norway
• 14.12. Poland
• 14.13. Qatar
• 14.14. Russia
• 14.15. Saudi Arabia
• 14.16. South Africa
• 14.17. Spain
• 14.18. Sweden
• 14.19. Switzerland
• 14.20. Turkey
• 14.21. United Arab Emirates
• 14.22. United Kingdom

15. Competitive Landscape

• 15.1. Market Share Analysis, 2024
• 15.2. FPNV Positioning Matrix, 2024
• 15.3. Competitive Scenario Analysis
  • 15.3.1. GE HealthCare unveils AI innovation lab to revolutionize medical technology
  • 15.3.2. Stryker's strategic acquisition of care.ai to enhance AI-driven healthcare solutions
  • 15.3.3. GE HealthCare strengthens ultrasound capabilities with AI acquisition
• 15.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Aidoc Medical Ltd.
• 2. Allscripts Healthcare Solutions, Inc.
• 3. BenevolentAI Limited
• 4. Butterfly Network, Inc.
• 5. CloudMedx Inc.
• 6. Enlitic, Inc.
• 7. Epic Systems Corporation
• 8. Exscientia plc
• 9. Freenome Holdings, Inc.
• 10. GE Healthcare
• 11. Google LLC By Alphabet Inc.
• 12. HeartFlow, Inc.
• 13. IBM Corporation
• 14. Insilico Medicine, Inc.
• 15. Intel Corporation
• 16. Koninklijke Philips N.V.
• 17. Medtronic plc
• 18. NVIDIA Corporation
• 19. Owkin, Inc.
• 20. PathAI, Inc.
• 21. Qventus, Inc.
• 22. Recursion Pharmaceuticals, Inc.
• 23. Siemens Healthineers AG
• 24. Tempus Labs, Inc.
• 25. Viz.ai, Inc.
• 26. Zebra Medical Vision Ltd.