세계의 바이오메디컬용 인공지능(AI) 시장 : 컴포넌트, 기술, 비즈니스 기능, 용도, 최종사용자, 전개 모드별 - 예측(2025-2030년)

The Artificial Intelligence in Biomedical Market was valued at USD 2.87 billion in 2024 and is projected to grow to USD 3.26 billion in 2025, with a CAGR of 14.65%, reaching USD 6.53 billion by 2030.

The rapid evolution of artificial intelligence is not merely a technological advancement; it represents a paradigm shift in the biomedical sphere. Over the past decade, breakthroughs in machine learning, data analytics, and computational biology have redefined how research is conducted, diagnostics are made, and patient care is delivered. This transformation is bolstered by an integration of multidisciplinary expertise and an ever-increasing volume of biomedical data, making AI an indispensable tool in accelerating innovation.

In this evolving landscape, professionals and decision-makers are consistently challenged with discerning where to invest resources for maximum impact. The narrative that emerges is one of increased collaboration between technology and healthcare, where advanced algorithms work hand in hand with traditional biomedical methods. As a result, the convergence of these realms is not only enhancing operational efficiency but also paving the way for personalized medicine and predictive analytics.

The following sections provide a comprehensive overview of the key shifts, segmentation insights, regional dynamics, and corporate strategies that drive this intersection of AI and biomedical applications. Each segment has been carefully examined to present a holistic view of a rapidly changing industry, one that harnesses the power of digital transformation to foster breakthroughs in clinical practice and research.

Transformative Shifts in the Landscape: Redefining Biomedical AI

In recent years, the biomedical industry has witnessed transformative shifts that have redefined both research and therapeutic approaches. Advanced algorithmic models and an influx of computational power have enabled faster, more accurate predictions in diagnostics and drug development. This transformation is driven by a profound integration between technology platforms and healthcare methodologies, where digital tools now standardize tasks once considered exclusive to human expertise.

A major driver in this shift has been the maturation of machine learning techniques which, when combined with large datasets, have significantly reduced the time required for clinical decision-making processes. Enhanced data visualization and advanced analytics empower stakeholders to identify subtle trends that were previously undetectable. These developments facilitate a transition from reactive care to proactive intervention strategies, ultimately driving better patient outcomes.

Moreover, this new era of biomedical innovation is supported by the integration of cloud computing, edge devices, and interconnected systems that allow for secure data sharing and a more holistic approach to patient monitoring. As technologies like natural language processing and robotic process automation mature, they offer scalable solutions that transform everyday operations into intelligent, self-optimizing ecosystems. This leap forward is not simply a matter of incremental improvement but a comprehensive rethinking of how biomedical research is executed and how healthcare is delivered.

Key Segmentation Insights: A Deep Dive into Market Dimensions

The segmentation insights provide an extensive framework that helps in understanding the diverse facets of the biomedical AI market. The analysis based on component highlights the division into hardware, services, and software, with hardware further dissected into memory, network devices, and processors. The services component is analyzed with a focus on consulting, implementation, integration, and maintenance, while the software category is examined across applications, middleware, and platforms. These layers underscore the multifaceted nature of technological integration and operational support.

When exploring the market based on technology, the field is segmented into computer vision, machine learning, natural language processing, and robotic process automation. Computer vision itself is studied through functionalities like facial recognition, image recognition, and pattern recognition. Machine learning is further divided into deep learning, reinforcement learning, supervised learning, and unsupervised learning, ensuring that every analytic nuance is captured. In parallel, natural language processing delves into chatbots, language translation, speech recognition, and text analysis, and robotic process automation is categorized by attended automation and unattended automation.

The segmentation based on business function reveals its own intricacies by focusing on customer service, finance, and operations. Customer service involves customer feedback analysis and personalized support, finance centers on fraud detection and risk management, and operations encapsulate process optimization and resource allocation. In addition to these dimensions, the application segmentation categorizes the market into clinical trials, diagnostics, patient monitoring, and therapeutics; with clinical trials covering data analysis and recruitment, diagnostics exploring pathology and radiology, patient monitoring looking at remote monitoring methods and wearable devices, and therapeutics emphasizing drug discovery and precision medicine.

Further analysis based on end user identifies segments such as academic and research institutes, government agencies, healthcare providers, and pharmaceutical companies. These segments are further refined into research centers and universities, public health organizations and regulatory bodies, clinics and hospitals, and biotech versus medtech companies respectively. Finally, the deployment mode segmentation distinguishes between cloud-based and on-premise setups, with cloud-based models diving into hybrid cloud, private cloud, and public cloud frameworks. The totality of these segmentation dimensions provides an intricate roadmap for stakeholders to precisely tailor their strategies in the biomedical AI market.

Based on Component, market is studied across Hardware, Services, and Software. The Hardware is further studied across Memory, Network Devices, and Processors. The Services is further studied across Consulting, Implementation, Integration, and Maintenance. The Software is further studied across Applications, Middleware, and Platforms.

Based on Technology, market is studied across Computer Vision, Machine Learning, Natural Language Processing, and Robotic Process Automation. The Computer Vision is further studied across Facial Recognition, Image Recognition, and Pattern Recognition. The Machine Learning is further studied across Deep Learning, Reinforcement Learning, Supervised Learning, and Unsupervised Learning. The Natural Language Processing is further studied across Chatbots, Language Translation, Speech Recognition, and Text Analysis. The Robotic Process Automation is further studied across Attended Automation and Unattended Automation.

Based on Business Function, market is studied across Customer Service, Finance, and Operations. The Customer Service is further studied across Customer Feedback Analysis and Personalized Support. The Finance is further studied across Fraud Detection and Risk Management. The Operations is further studied across Process Optimization and Resource Allocation.

Based on Application, market is studied across Clinical Trials, Diagnostics, Patient Monitoring, and Therapeutics. The Clinical Trials is further studied across Data Analysis and Recruitment. The Diagnostics is further studied across Pathology and Radiology. The Patient Monitoring is further studied across Remote Monitoring and Wearable Devices. The Therapeutics is further studied across Drug Discovery and Precision Medicine.

Based on End User, market is studied across Academic and Research Institutes, Government Agencies, Healthcare Providers, and Pharmaceutical Companies. The Academic and Research Institutes is further studied across Research Centers and Universities. The Government Agencies is further studied across Public Health Organizations and Regulatory Bodies. The Healthcare Providers is further studied across Clinics and Hospitals. The Pharmaceutical Companies is further studied across Biotech Companies and Medtech Companies.

Based on Deployment Mode, market is studied across Cloud-Based and On-Premise. The Cloud-Based is further studied across Hybrid Cloud, Private Cloud, and Public Cloud.

Key Regional Insights: Dynamics Across Global Markets

Examining regional trends reveals that market dynamics vary significantly across different parts of the world. In the Americas, robust innovation ecosystems and significant investment in health technology research are creating favorable conditions for rapid adoption of AI in biomedical applications. High levels of funding and a well-established digital infrastructure further reinforce this region's leading role.

Europe, Middle East & Africa is characterized by diverse regulatory environments that necessitate careful navigation. While Europe is often at the forefront of stringent regulatory standards and ethical guidelines, the Middle East and Africa are emerging as dynamic spaces where governmental initiatives and investments in public health are catalyzing the spread of smart technologies. This combination of tight governance and innovation-led public projects supports sustainable growth in biomedical AI strategies.

In the Asia-Pacific region, the emphasis is on scaling technologies to meet rising healthcare demands, underpinned by the rapid embrace of digital solutions. The region benefits from a large pool of tech-savvy professionals and cost-effective innovation, making it a hotbed for breakthrough applications in patient monitoring, diagnostics, and therapeutics. Each of these regions presents unique opportunities and challenges that industry players must address to fully leverage the transformative potential of AI in biomedicine.

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.

Key Companies Insights: Leaders Pioneering Biomedical AI

A detailed review of key companies in the biomedical AI market provides a vivid picture of the competitive landscape. Leading organizations such as AiCure, LLC; Arterys Inc.; Aspen Technology Inc; Atomwise Inc; and Augmedix, Inc. are driving innovation by merging advanced technology with healthcare objectives. Firms like Behold.ai Technologies Limited, BenevolentAI SA, and BioSymetrics Inc. are forging ahead with state-of-the-art solutions in pattern and image recognition, as well as predictive analytics.

Other pioneering companies including BPGbio Inc., Butterfly Network, Inc., and Caption Health, Inc. by GE Healthcare have made significant contributions towards integrating AI with medical imaging and diagnostic protocols. Cloud Pharmaceuticals, Inc., CloudMedX Inc., and Corti ApS are at the forefront of leveraging cloud-based infrastructures and automated decision-making systems to streamline patient care and data management. Deep Genomics Incorporated, along with Cyclica Inc by Recursion Pharmaceuticals, Inc., further expands the narrative by pushing the boundaries of genomic research and molecular data analysis.

Notably, organizations such as Deargen Inc, Euretos BV, Exscientia plc, and Google, LLC by Alphabet, Inc. underscore the deep-rooted collaboration between tech giants and innovative startups. These synergistic partnerships illustrate how multi-disciplinary expertise is reshaping areas like drug discovery, diagnostic accuracy, and personalized medicine. Additional players like Insilico Medicine, Intel Corporation, International Business Machines Corporation, and InveniAI LLC illustrate the impressive array of corporate investment in the sector. Companies such as Isomorphic Labs, Novo Nordisk A/S, Sanofi SA, Turbine Ltd., Viseven Europe OU, and XtalPi Inc. round out this group of industry leaders consistently pushing the envelope on research and commercial innovations in the biomedical AI arena.

The report delves into recent significant developments in the Artificial Intelligence in Biomedical Market, highlighting leading vendors and their innovative profiles. These include AiCure, LLC, Arterys Inc., Aspen Technology Inc, Atomwise Inc, Augmedix, Inc., Behold.ai Technologies Limited, BenevolentAI SA, BioSymetrics Inc., BPGbio Inc., Butterfly Network, Inc., Caption Health, Inc. by GE Healthcare, Cloud Pharmaceuticals, Inc., CloudMedX Inc., Corti ApS, Cyclica Inc by Recursion Pharmaceuticals, Inc., Deargen Inc, Deep Genomics Incorporated, Euretos BV, Exscientia plc, Google, LLC by Alphabet, Inc., Insilico Medicine, Intel Corporation, International Business Machines Corporation, InveniAI LLC, Isomorphic Labs, Novo Nordisk A/S, Sanofi SA, Turbine Ltd., Viseven Europe OU, and XtalPi Inc.. Actionable Recommendations: Strategic Guidance for Industry Leaders

Leaders operating in the dynamic landscape of biomedical AI must adopt agile strategies and invest in forward-thinking technologies. First, it is essential to continuously update technical infrastructure while emphasizing robust cybersecurity measures to protect sensitive health data. Upgrading to systems that support hybrid cloud configurations can offer a balanced approach, delivering both the scalability of public cloud services and the security of private systems.

Second, fostering partnerships between healthcare providers and technology innovators is pivotal. Industry players should initiate cross-disciplinary collaborations that include academic institutions, government agencies, and leading tech companies. Such partnerships not only expedite the development of breakthrough solutions but also ensure that these innovations are grounded in rigorous scientific methodologies.

Third, companies should allocate dedicated resources towards talent development and retention. Continuous professional training in the areas of machine learning, data analytics, and biomedical research will equip teams with the skills required to keep pace with rapidly evolving technologies. Investment in employee education, along with strategic hires, will bolster the capacity for research and operational efficiency.

Furthermore, organizations must regularly analyze market segmentation trends, adjusting product portfolios to meet diverse customer needs. By deploying comprehensive analyses that consider components such as hardware, services, software, and specific technological applications, companies can pivot swiftly in response to emerging demands. In addition, strategic geographical expansion should be considered, with special attention paid to regions showing high growth potential and favorable regulatory environments. These consolidated recommendations can serve as a roadmap for long-term strategic planning and competitive positioning.

Conclusion: Embracing a Data-Driven Future in Biomedical AI

In summary, the penetration of artificial intelligence into the biomedical arena has profoundly reshaped the way research, diagnostics, and patient care are approached. The landscape is undergoing a significant evolution, driven by technological advancements and a growing emphasis on data-driven decision-making. Detailed market segmentation reinforces how multifaceted the industry is, outlining clear distinctions based on component, technology, business function, application, end user, and deployment mode. At the regional level, variations in economic, regulatory, and demographic conditions underline the need for tailored strategies.

Companies operating in this dynamic environment illustrate a strong commitment to innovation and collaboration. Their ability to continuously integrate advanced technologies with traditional biomedical processes is setting the stage for transformative advancements in precision medicine and patient care. As the market matures, stakeholders must remain proactive in adapting to change and leveraging opportunities presented by emerging technologies.

This evolving narrative of biomedical AI, underpinned by comprehensive market segmentation and supported by a global network of key players, points towards a future where health systems become smarter, more efficient, and highly personalized. The journey ahead is challenging but filled with potential, and now is the time to harness these innovations to drive meaningful progress in 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. Growing need for improved diagnostic tools and patient outcomes advancing
    • 5.1.1.2. Rising demand for personalized medicine boosting AI implementation in biomedical practices
    • 5.1.1.3. Surge in healthcare data generation
  • 5.1.2. Restraints
    • 5.1.2.1. Limitations of current AI algorithms in accurately understanding complex biological systems
  • 5.1.3. Opportunities
    • 5.1.3.1. Applying natural language processing to enhance clinical decision-making and data analysis
    • 5.1.3.2. Enhancing research and development through AI-driven computational models and simulations
  • 5.1.4. Challenges
    • 5.1.4.1. Managing data bias and accuracy concerns in AI applications within biomedicine
• 5.2. Market Segmentation Analysis
  • 5.2.1. Technology : Transformative impact of AI technologies on biomedical innovations and commercial strategies
  • 5.2.2. Application : Proliferation of AI applications in both pathology and radiology owing to their diagnostic accuracy and speed
• 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 Biomedical Market, by Component

• 6.1. Introduction
• 6.2. Hardware
  • 6.2.1. Memory
  • 6.2.2. Network Devices
  • 6.2.3. Processors
• 6.3. Services
  • 6.3.1. Consulting
  • 6.3.2. Implementation
  • 6.3.3. Integration
  • 6.3.4. Maintenance
• 6.4. Software
  • 6.4.1. Applications
  • 6.4.2. Middleware
  • 6.4.3. Platforms

7. Artificial Intelligence in Biomedical Market, by Technology

• 7.1. Introduction
• 7.2. Computer Vision
  • 7.2.1. Facial Recognition
  • 7.2.2. Image Recognition
  • 7.2.3. Pattern Recognition
• 7.3. Machine Learning
  • 7.3.1. Deep Learning
  • 7.3.2. Reinforcement Learning
  • 7.3.3. Supervised Learning
  • 7.3.4. Unsupervised Learning
• 7.4. Natural Language Processing
  • 7.4.1. Chatbots
  • 7.4.2. Language Translation
  • 7.4.3. Speech Recognition
  • 7.4.4. Text Analysis
• 7.5. Robotic Process Automation
  • 7.5.1. Attended Automation
  • 7.5.2. Unattended Automation

8. Artificial Intelligence in Biomedical Market, by Business Function

• 8.1. Introduction
• 8.2. Customer Service
  • 8.2.1. Customer Feedback Analysis
  • 8.2.2. Personalized Support
• 8.3. Finance
  • 8.3.1. Fraud Detection
  • 8.3.2. Risk Management
• 8.4. Operations
  • 8.4.1. Process Optimization
  • 8.4.2. Resource Allocation

9. Artificial Intelligence in Biomedical Market, by Application

• 9.1. Introduction
• 9.2. Clinical Trials
  • 9.2.1. Data Analysis
  • 9.2.2. Recruitment
• 9.3. Diagnostics
  • 9.3.1. Pathology
  • 9.3.2. Radiology
• 9.4. Patient Monitoring
  • 9.4.1. Remote Monitoring
  • 9.4.2. Wearable Devices
• 9.5. Therapeutics
  • 9.5.1. Drug Discovery
  • 9.5.2. Precision Medicine

10. Artificial Intelligence in Biomedical Market, by End User

• 10.1. Introduction
• 10.2. Academic and Research Institutes
  • 10.2.1. Research Centers
  • 10.2.2. Universities
• 10.3. Government Agencies
  • 10.3.1. Public Health Organizations
  • 10.3.2. Regulatory Bodies
• 10.4. Healthcare Providers
  • 10.4.1. Clinics
  • 10.4.2. Hospitals
• 10.5. Pharmaceutical Companies
  • 10.5.1. Biotech Companies
  • 10.5.2. Medtech Companies

11. Artificial Intelligence in Biomedical Market, by Deployment Mode

• 11.1. Introduction
• 11.2. Cloud-Based
  • 11.2.1. Hybrid Cloud
  • 11.2.2. Private Cloud
  • 11.2.3. Public Cloud
• 11.3. On-Premise

12. Americas Artificial Intelligence in Biomedical 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 Biomedical 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 Biomedical 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. Strategic agreement between Recursion and Exscientia to enhance AI-driven drug discovery process
  • 15.3.2. Insilico Medicine and NVIDIA launch nach0 transformer to enhance AI-driven biomedical discoveries
  • 15.3.3. HCA Healthcare's adoption of AI-powered Augmedix Go transforms emergency department documentation
  • 15.3.4. Bionl.AI Launches Innovative Platform for No-Code Biomedical Research
  • 15.3.5. Epistemic AI launches biomedical GPT at BIO
  • 15.3.6. NIH launches Bridge2AI program to expand the use of artificial intelligence in biomedical and behavioral research
• 15.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. AiCure, LLC
• 2. Arterys Inc.
• 3. Aspen Technology Inc
• 4. Atomwise Inc
• 5. Augmedix, Inc.
• 6. Behold.ai Technologies Limited
• 7. BenevolentAI SA
• 8. BioSymetrics Inc.
• 9. BPGbio Inc.
• 10. Butterfly Network, Inc.
• 11. Caption Health, Inc. by GE Healthcare
• 12. Cloud Pharmaceuticals, Inc.
• 13. CloudMedX Inc.
• 14. Corti ApS
• 15. Cyclica Inc by Recursion Pharmaceuticals, Inc.
• 16. Deargen Inc
• 17. Deep Genomics Incorporated
• 18. Euretos BV
• 19. Exscientia plc
• 20. Google, LLC by Alphabet, Inc.
• 21. Insilico Medicine
• 22. Intel Corporation
• 23. International Business Machines Corporation
• 24. InveniAI LLC
• 25. Isomorphic Labs
• 26. Novo Nordisk A/S
• 27. Sanofi SA
• 28. Turbine Ltd.
• 29. Viseven Europe OU
• 30. XtalPi Inc.