맞춤형 의료용 AI(인공지능) 시장 예측(-2034년) : 구성요소별, 기술별, 치료 분야별, 데이터 종류별, 용도별, 최종사용자별, 지역별 세계 분석

According to Stratistics MRC, the Global AI in Personalized Medicine Market is accounted for $2.8 billion in 2026 and is expected to reach $57.3 billion by 2034, growing at a CAGR of 38.2% during the forecast period. AI in Personalized Medicine involves leveraging machine learning and data-driven techniques to customize healthcare for each patient. By examining extensive genetic, clinical, and lifestyle information, AI systems can forecast disease likelihood, recommend optimal therapies, and improve treatment effectiveness. This approach advances precision medicine by enhancing diagnostic precision, minimizing side effects, and assisting healthcare providers in delivering individualized care. Ultimately, it empowers more accurate, efficient, and patient-focused medical decision-making.

Market Dynamics:

Driver:

Exponential growth in genomic and multi-omics data

The exponential growth in genomic and multi-omics data is a primary driver for AI integration. As sequencing costs decline, the volume of genetic information available for analysis has surged. AI algorithms, particularly machine learning, are uniquely capable of processing these vast, complex datasets to identify disease markers and predict drug responses. This capability enables the shift from traditional trial-and-error medicine to precise therapeutic interventions. Furthermore, the increasing demand for targeted therapies in oncology and rare diseases necessitates AI-driven analytics to match patients with the most effective treatments, accelerating the adoption of personalized medicine solutions.

Restraint: Data privacy concerns and lack of interoperability

Significant challenges arise from data privacy concerns and the lack of standardized data interoperability. Healthcare data is highly sensitive, and navigating regulations like HIPAA and GDPR creates complexity for AI developers. Additionally, fragmented electronic health record (EHR) systems often store data in siloed, incompatible formats, hindering the creation of large, unified datasets required to train robust AI models. The "black box" nature of some AI algorithms also poses a barrier to clinical adoption, as physicians often require explainable outputs to trust AI-driven recommendations for patient care, slowing integration into clinical workflows.

Opportunity: Integration with wearables and IoT devices

The integration of AI with wearable health monitoring devices and the Internet of Things (IoT) presents a significant growth opportunity. Continuous streams of real-world data from smartwatches and implantable sensors allow AI models to monitor patient health dynamically, predict adverse events, and adjust treatment plans in real-time. This capability is particularly valuable for managing chronic diseases like diabetes and cardiovascular conditions. Moreover, the expansion of telehealth and remote patient monitoring creates a fertile ground for AI-powered platforms that can deliver personalized care outside traditional hospital settings, improving accessibility and patient engagement.

Threat: Algorithmic bias and regulatory uncertainty

Algorithmic bias poses a critical threat to the equitable deployment of AI in personalized medicine. If AI models are trained predominantly on datasets from specific demographic groups, their predictive accuracy may be significantly lower for underrepresented populations. This can lead to misdiagnosis or ineffective treatment recommendations for minority groups, exacerbating existing healthcare disparities. Additionally, the rapid pace of AI development often outstrips the regulatory frameworks designed to ensure safety and efficacy, creating uncertainty for developers and potential risks for patients if unvalidated tools are adopted prematurely.

Covid-19 Impact

The pandemic acted as a powerful catalyst for AI adoption in personalized medicine. The urgent need for rapid vaccine development and repurposing of existing drugs saw AI used to analyze viral genomics and host responses at unprecedented speeds. Lockdowns accelerated the adoption of telemedicine and remote monitoring, driving demand for AI tools to manage patient data remotely. However, the crisis also overwhelmed healthcare systems, diverting resources from non-COVID research and delaying some clinical trials for AI-based diagnostics. Post-pandemic, there is a sustained focus on building resilient, AI-driven healthcare systems capable of rapid, personalized responses to future health crises.

The software segment is expected to be the largest during the forecast period

The software segment, particularly AI analytics platforms and clinical decision support systems (CDSS), is expected to account for the largest market share. This dominance is driven by the foundational role of software in processing complex genomic and clinical data to generate actionable insights. Hospitals and research institutes are heavily investing in these platforms to enhance diagnostic accuracy and streamline drug discovery. The scalability and continuous upgradability of cloud-based software solutions further solidify their market leadership, as they form the core infrastructure for any personalized medicine initiative.

The hardware segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the hardware segment is predicted to witness the highest growth rate, driven by the increasing need for high-performance computing (HPC) infrastructure. The immense computational power required to train deep learning models on genomic and imaging datasets is fueling demand for advanced processors and AI-enabled medical devices. Additionally, the proliferation of wearable health monitoring devices that generate personalized patient data is contributing to this rapid expansion. As AI algorithms become more complex, the demand for specialized hardware to support them will continue to accelerate.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by substantial R&D investments, a strong presence of key technology players, and a sophisticated healthcare infrastructure. The United States, in particular, leads in the adoption of AI-driven genomic testing and digital therapeutics. Favorable reimbursement frameworks for personalized medicine and high healthcare expenditure support the integration of advanced AI tools into clinical practice, solidifying the region's dominant position.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid digitalization of healthcare systems, large patient populations generating vast datasets, and increasing government initiatives for precision medicine. Countries like China, Japan, and India are investing heavily in genomics research and AI infrastructure. The growing prevalence of chronic diseases and a burgeoning medical tourism sector are accelerating the adoption of advanced AI technologies to offer personalized and efficient care, driving significant market expansion.

Key players in the market

Some of the key players in AI in Personalized Medicine Market include NVIDIA Corporation, Google LLC, Microsoft Corporation, IBM Corporation, Illumina, Inc., GE HealthCare, Siemens Healthineers AG, Tempus AI, Exscientia plc, Insilico Medicine, BenevolentAI, PathAI, Inc., Guardant Health, Inc., Deep Genomics, and Paige AI, Inc.

Key Developments:

In March 2026, IBM and ETH Zurich announced a 10-year collaboration to advance the next generation of algorithms at the intersection of AI and quantum computing. This initiative represents the latest milestone in the long-standing collaboration between the two institutions, further strengthening a scientific exchange that has helped create the future of information technology.

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

Components Covered:

• Software
• Hardware
• Services

Technologies Covered:

• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Context-Aware AI Processing
• Expert Systems

Therapeutic Areas Covered:

• Oncology
• Cardiology
• Neurology
• Infectious Diseases
• Rare Diseases
• Respiratory Disorders

Data Types Covered:

• Genomic Data
• Clinical Data
• Imaging Data
• Real-World Data (RWD)
• Patient-Generated Data

Applications Covered:

• Drug Discovery & Development
• Genomics & Multi-Omics Analysis
• Clinical Decision Support
• Personalized Treatment Planning
• Biomarker Discovery
• Patient Monitoring & Predictive Analytics

End Users Covered:

• Hospitals & Healthcare Providers
• Pharmaceutical & Biotechnology Companies
• Research Institutes & Academic Centers
• Diagnostic Laboratories
• Contract Research Organizations (CROs)
• Other End Users

Regions Covered:

• North America
  • United States
  • Canada
  • Mexico
• Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Spain
  • Netherlands
  • Belgium
  • Sweden
  • Switzerland
  • Poland
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Thailand
  • Malaysia
  • Singapore
  • Vietnam
  • Rest of Asia Pacific
• South America
  • Brazil
  • Argentina
  • Colombia
  • Chile
  • Peru
  • Rest of South America
• Rest of the World (RoW)
  • Middle East
• Saudi Arabia
• United Arab Emirates
• Qatar
• Israel
• Rest of Middle East
  • Africa
• South Africa
• Egypt
• Morocco
• Rest of Africa

What our report offers:

• Market share assessments for the regional and country-level segments
• Strategic recommendations for the new entrants
• Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
• Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
• Strategic recommendations in key business segments based on the market estimations
• Competitive landscaping mapping the key common trends
• Company profiling with detailed strategies, financials, and recent developments
• Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

• Company Profiling
  • Comprehensive profiling of additional market players (up to 3)
  • SWOT Analysis of key players (up to 3)
• Regional Segmentation
  • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
  • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

• 1.1 Market Snapshot and Key Highlights
• 1.2 Growth Drivers, Challenges, and Opportunities
• 1.3 Competitive Landscape Overview
• 1.4 Strategic Insights and Recommendations

2 Research Framework

• 2.1 Study Objectives and Scope
• 2.2 Stakeholder Analysis
• 2.3 Research Assumptions and Limitations
• 2.4 Research Methodology
  • 2.4.1 Data Collection (Primary and Secondary)
  • 2.4.2 Data Modeling and Estimation Techniques
  • 2.4.3 Data Validation and Triangulation
  • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

• 3.1 Market Definition and Structure
• 3.2 Key Market Drivers
• 3.3 Market Restraints and Challenges
• 3.4 Growth Opportunities and Investment Hotspots
• 3.5 Industry Threats and Risk Assessment
• 3.6 Technology and Innovation Landscape
• 3.7 Emerging and High-Growth Markets
• 3.8 Regulatory and Policy Environment
• 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

• 4.1 Porter's Five Forces Analysis
  • 4.1.1 Supplier Bargaining Power
  • 4.1.2 Buyer Bargaining Power
  • 4.1.3 Threat of Substitutes
  • 4.1.4 Threat of New Entrants
  • 4.1.5 Competitive Rivalry
• 4.2 Market Share Analysis of Key Players
• 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Personalized Medicine Market, By Component

• 5.1 Software
  • 5.1.1 AI Analytics Platforms
  • 5.1.2 Genomic Analysis Software
  • 5.1.3 Clinical Decision Support Systems
  • 5.1.4 Predictive Modeling Tools
• 5.2 Hardware
  • 5.2.1 AI-Enabled Medical Devices
  • 5.2.2 High-Performance Computing Infrastructure
  • 5.2.3 Wearable Health Monitoring Devices
• 5.3 Services
  • 5.3.1 Consulting Services
  • 5.3.2 Integration & Deployment Services
  • 5.3.3 Maintenance & Support Services

6 Global AI in Personalized Medicine Market, By Technology

• 6.1 Machine Learning
  • 6.1.1 Deep Learning
  • 6.1.2 Neural Networks
  • 6.1.3 Random Forest Algorithms
  • 6.1.4 Support Vector Machines
• 6.2 Natural Language Processing (NLP)
  • 6.2.1 Clinical Text Mining
  • 6.2.2 Medical Entity Recognition
  • 6.2.3 Sentiment and Outcome Analysis
• 6.3 Computer Vision
• 6.4 Context-Aware AI Processing
• 6.5 Expert Systems
  • 6.5.1 Rule-Based Systems
  • 6.5.2 Decision Trees
  • 6.5.3 Bayesian Networks

7 Global AI in Personalized Medicine Market, By Therapeutic Area

• 7.1 Oncology
• 7.2 Cardiology
• 7.3 Neurology
• 7.4 Infectious Diseases
• 7.5 Rare Diseases
• 7.6 Respiratory Disorders

8 Global AI in Personalized Medicine Market, By Data Type

• 8.1 Genomic Data
• 8.2 Clinical Data
• 8.3 Imaging Data
• 8.4 Real-World Data (RWD)
• 8.5 Patient-Generated Data

9 Global AI in Personalized Medicine Market, By Application

• 9.1 Drug Discovery & Development
  • 9.1.1 Target Identification
  • 9.1.2 Molecular Modeling
  • 9.1.3 Virtual Screening
• 9.2 Genomics & Multi-Omics Analysis
  • 9.2.1 Genomics
  • 9.2.2 Proteomics
  • 9.2.3 Metabolomics
  • 9.2.4 Pharmacogenomics
• 9.3 Clinical Decision Support
  • 9.3.1 Diagnosis Support
  • 9.3.2 Treatment Selection
  • 9.3.3 Disease Risk Prediction
• 9.4 Personalized Treatment Planning
• 9.5 Biomarker Discovery
• 9.6 Patient Monitoring & Predictive Analytics

10 Global AI in Personalized Medicine Market, By End User

• 10.1 Hospitals & Healthcare Providers
• 10.2 Pharmaceutical & Biotechnology Companies
• 10.3 Research Institutes & Academic Centers
• 10.4 Diagnostic Laboratories
• 10.5 Contract Research Organizations (CROs)
• 10.6 Other End Users

11 Global AI in Personalized Medicine Market, By Geography

• 11.1 North America
  • 11.1.1 United States
  • 11.1.2 Canada
  • 11.1.3 Mexico
• 11.2 Europe
  • 11.2.1 United Kingdom
  • 11.2.2 Germany
  • 11.2.3 France
  • 11.2.4 Italy
  • 11.2.5 Spain
  • 11.2.6 Netherlands
  • 11.2.7 Belgium
  • 11.2.8 Sweden
  • 11.2.9 Switzerland
  • 11.2.10 Poland
  • 11.2.11 Rest of Europe
• 11.3 Asia Pacific
  • 11.3.1 China
  • 11.3.2 Japan
  • 11.3.3 India
  • 11.3.4 South Korea
  • 11.3.5 Australia
  • 11.3.6 Indonesia
  • 11.3.7 Thailand
  • 11.3.8 Malaysia
  • 11.3.9 Singapore
  • 11.3.10 Vietnam
  • 11.3.11 Rest of Asia Pacific
• 11.4 South America
  • 11.4.1 Brazil
  • 11.4.2 Argentina
  • 11.4.3 Colombia
  • 11.4.4 Chile
  • 11.4.5 Peru
  • 11.4.6 Rest of South America
• 11.5 Rest of the World (RoW)
  • 11.5.1 Middle East
    • 11.5.1.1 Saudi Arabia
    • 11.5.1.2 United Arab Emirates
    • 11.5.1.3 Qatar
    • 11.5.1.4 Israel
    • 11.5.1.5 Rest of Middle East
  • 11.5.2 Africa
    • 11.5.2.1 South Africa
    • 11.5.2.2 Egypt
    • 11.5.2.3 Morocco
    • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

• 12.1 Industry Value Network and Supply Chain Assessment
• 12.2 White-Space and Opportunity Mapping
• 12.3 Product Evolution and Market Life Cycle Analysis
• 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

• 13.1 Mergers and Acquisitions
• 13.2 Partnerships, Alliances, and Joint Ventures
• 13.3 New Product Launches and Certifications
• 13.4 Capacity Expansion and Investments
• 13.5 Other Strategic Initiatives

14 Company Profiles

• 14.1 NVIDIA Corporation
• 14.2 Google LLC
• 14.3 Microsoft Corporation
• 14.4 IBM Corporation
• 14.5 Illumina, Inc.
• 14.6 GE HealthCare
• 14.7 Siemens Healthineers AG
• 14.8 Tempus AI
• 14.9 Exscientia plc
• 14.10 Insilico Medicine
• 14.11 BenevolentAI
• 14.12 PathAI, Inc.
• 14.13 Guardant Health, Inc.
• 14.14 Deep Genomics
• 14.15 Paige AI, Inc.