정밀의료용 인공지능(AI) 시장 : 시장 규모 및 점유율 산업 분석(컴포넌트별, 기술별, 치료 용도별, 지역별), 전망 및 예측(2026-2033년)

The Global Artificial Intelligence In Precision Medicine Market is expected to reach USD 31628.7 million by 2033, growing at a CAGR of 37.8% during (2026 - 2033).

Rising demand for personalized treatment strategies, faster disease diagnosis, data-driven clinical decision-making, and efficient drug discovery is driving adoption of AI in precision medicine across healthcare providers, pharmaceutical companies, biotechnology firms, and research institutions. Growth is further strengthened by expanding genomics and multi-omics data, digital health adoption, biomarker discovery, AI-assisted diagnostics, patient stratification, therapy selection, and integrated clinical decision-support platforms. The market originated from the convergence of computational biology, genomics, and machine learning technologies. Early applications focused on basic analytics for genetic profiles, patient records, and clinical data interpretation.

Key Market Trends & Insights

• By component, Software dominated the market in 2025 with USD 1,065.3 million and is projected to reach USD 12,970.9 million by 2033, growing at a CAGR of 37.2% during (2026 - 2033).
• Services is expected to grow fastest by component, registering a CAGR of 38.3% during (2026 - 2033), supported by AI implementation, system integration, workflow customization, consulting, and clinical deployment support.
• By technology, Deep Learning dominated the market in 2025 with USD 847.6 million and is projected to reach USD 10,071.9 million by 2033, growing at a CAGR of 36.8% during (2026 - 2033).
• Context Aware Processing is expected to grow fastest by technology, registering a CAGR of 38.7% during (2026 - 2033), driven by patient-specific recommendations, real-time clinical context, wearable data, and adaptive care pathways.
• Natural Language Processing is projected to reach USD 9,898.6 million by 2033, growing at a CAGR of 38.0% during (2026 - 2033), supported by clinical-note analysis, EHR interpretation, literature mining, and patient stratification.
• By therapeutic application, Oncology dominated the market in 2025 with USD 790.2 million and is projected to reach USD 9,347.6 million by 2033, growing at a CAGR of 36.7% during (2026 - 2033).
• Other Therapeutic Application is expected to grow fastest by therapeutic application, registering a CAGR of 39.6% during (2026 - 2033), supported by rising AI use in rare diseases, infectious diseases, metabolic disorders, immunology, and pharmacogenomics.
• Regionally, North America dominated the market in 2025 with USD 1,342.8 million and is projected to reach USD 16,469.0 million by 2033, while LAMEA is expected to grow fastest with a CAGR of 41.3% during (2026 - 2033).

Rising use of AI in precision medicine is driven by the healthcare industry's shift from generalized treatment models toward individualized, data-driven care. AI enables clinicians and researchers to analyze complex biological and clinical datasets that are difficult to interpret through conventional methods. Growing use of genomic sequencing, digital pathology, wearable data, imaging analytics, and real-world evidence is strengthening the role of AI in diagnosis, prognosis, treatment planning, and patient monitoring. Pharmaceutical and biotechnology companies are also using AI to improve target discovery, trial design, biomarker validation, and personalized therapeutic development.

The competitive environment is moderately consolidated yet highly innovation-driven, with specialized AI healthcare companies, diagnostics firms, pharmaceutical-backed platforms, and computational biology players competing across precision oncology, genomics, real-world evidence, digital pathology, and AI-enabled drug discovery. Competition is shaped by the ability to access high-quality proprietary datasets, build clinically validated models, integrate with healthcare workflows, and comply with evolving medical AI regulations. Strategic partnerships between AI developers, hospitals, pharmaceutical companies, diagnostic labs, and research institutions are becoming essential for scaling AI-enabled precision medicine solutions.

Drivers

• Transformative Impact of AI-Driven Biomarker Discovery on Precision Medicine
• Acceleration of AI-Enabled Drug Discovery and Development Processes
• Enhanced Clinical Decision Support through AI-Integrated Diagnostic and Treatment Tools
• Expansion of AI Applications in Cell and Gene Therapy for Precision Medicine

Restraints

• Regulatory Complexity and Uncertainty in AI-Driven Precision Medicine
• High Implementation and Operational Costs as a Barrier to Market Penetration
• Data Privacy, Security Concerns, and Integration Challenges Limiting Market Expansion

Opportunities

• AI-Enabled Multimodal Data Integration for Enhanced Risk Stratification and Diagnosis
• AI-Driven Drug Discovery and Development Acceleration for Personalized Therapeutics
• Integration of AI with Real-Time Patient Monitoring and Wearable Health Technologies

Challenges

• Data Privacy and Security Concerns in Integrating AI with Sensitive Patient Information
• High Development and Implementation Costs Restricting Market Expansion
• Technical Limitations in Multi-Modal Data Integration and AI Model Interpretability

Market Share Analysis

The global Artificial Intelligence In Precision Medicine Market reflects a highly competitive and fragmented landscape, led by specialized precision oncology, genomic analytics, digital pathology, real-world evidence, and computational drug discovery companies. Tempus AI, Caris Life Sciences, Guardant Health, Roche, ConcertAI, SOPHiA GENETICS, PathAI, Owkin, Recursion Pharmaceuticals, and Personalis hold important positions through differentiated strengths in multimodal datasets, biomarker discovery, clinical validation, and AI-enabled therapeutic development. Market leadership is increasingly determined by proprietary healthcare data access, regulatory readiness, interoperability, explainability, and partnerships with hospitals, pharmaceutical companies, and research institutions.

Component Outlook

Based on Component, the market is segmented into Software, Services, and Hardware. The Software market dominated the Global Artificial Intelligence In Precision Medicine Market by Component in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 12970.9 million by 2033, growing at a CAGR of 37.2 % during the forecast period. The Services market is expected to witness a CAGR of 38.3% during (2026 - 2033). The Hardware market is expected to witness a CAGR of 38.2% during (2026 - 2033).

Software forms the core layer of AI-enabled precision medicine as it includes algorithms, predictive analytics platforms, clinical decision-support tools, biomarker discovery solutions, and data interpretation systems. Services are essential for implementation, integration, consulting, workflow customization, regulatory support, and ongoing platform maintenance. Hardware supports the computational and diagnostic backbone of the market through high-performance computing, sequencing systems, imaging infrastructure, servers, and secure data-processing environments. Together, these components enable the collection, analysis, validation, and clinical use of complex patient-specific datasets.

Technology Outlook

Based on Technology, the market is segmented into Deep Learning, Natural Language Processing, Querying Method, and Context Aware Processing. The Deep Learning market dominated the Global Artificial Intelligence In Precision Medicine Market by Technology in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 10071.0 million by 2033, growing at a CAGR of 36.8 % during the forecast period. The Natural Language Processing market is expected to witness a CAGR of 38% during (2026 - 2033). Additionally, The Querying Method market is expected to witness highest CAGR of 38.5% during (2026 - 2033).

Deep Learning plays a major role in analyzing imaging, genomic, pathology, and multi-omics datasets where complex pattern recognition is required. Natural Language Processing supports extraction of insights from clinical notes, electronic health records, research literature, and unstructured medical documents. Querying Method enables efficient retrieval and interpretation of large biomedical databases, clinical repositories, and patient records. Context Aware Processing strengthens precision medicine by linking patient-specific clinical, molecular, demographic, and treatment-context information to generate more relevant care recommendations.

Therapeutic Application Outlook

Based on Therapeutic Application, the market is segmented into Oncology, Cardiology, Neurology, Respiratory, and Other Therapeutic Application. The Oncology market dominated the Global Artificial Intelligence In Precision Medicine Market by Therapeutic Application in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 9347.7 million by 2033, growing at a CAGR of 36.7 % during the forecast period. The Cardiology market is expected to witness a CAGR of 37.5% during (2026 - 2033). Additionally, The Neurology market is expected to witness highest CAGR of 38.2% during (2026 - 2033).

Oncology remains a highly prominent application area due to the strong use of genomic profiling, tumor characterization, biomarker discovery, liquid biopsy, and companion diagnostics. Cardiology applications are expanding through risk prediction, disease progression monitoring, imaging analytics, and patient-specific treatment planning. Neurology benefits from AI-driven analysis of neuroimaging, cognitive data, genetic markers, and disease progression patterns. Respiratory applications are gaining relevance through early disease detection, pulmonary risk assessment, remote monitoring, and personalized care for chronic respiratory conditions, while other therapeutic areas include rare diseases, infectious diseases, immunology, metabolic disorders, and pharmacogenomics.

Regional Outlook

Region-wise, the Artificial Intelligence In Precision Medicine Market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America market dominated the Global Artificial Intelligence In Precision Medicine Market by Region in 2025, and would continue to be a dominant market till 2033; thereby, achieving a market value of USD 16469.1 million by 2033, growing at a CAGR of 37.3 % during the forecast period. The Europe market is expected to witness a CAGR of 37.5% during (2026 - 2033). Additionally, The Asia Pacific market is expected to witness a CAGR of 38.8% during (2026 - 2033).

North America remains a major regional market due to strong healthcare data infrastructure, advanced biopharma activity, high AI investment, and early adoption of precision oncology and clinical decision-support platforms. Europe benefits from healthcare digitization, multi-omics research, public funding, structured data-governance frameworks, and growing regulatory clarity around responsible AI adoption. Asia Pacific is expanding through rising genomic research, healthcare AI initiatives, digital health adoption, and increasing precision medicine investments in countries such as China, Japan, India, South Korea, Singapore, and Malaysia. LAMEA is developing steadily as healthcare modernization, AI diagnostics, remote monitoring, and localized precision medicine initiatives gain momentum.

Recent Strategies Deployed in the Market

• 2026-August: SOPHiA GENETICS entered a multi-year global collaboration with AstraZeneca to develop, validate, and deploy companion diagnostics for precision oncology.
• 2026-July: Tempus AI agreed to acquire Personalis, combining tumor-informed minimal residual disease technology with multimodal data and AI-enabled precision oncology capabilities.
• 2026-June: SOPHiA GENETICS and Memorial Sloan Kettering planned an AI Lab of the Future for next-generation precision oncology, combining clinical, genomic, pathology, and radiology datasets with an AI-native platform.
• 2026-May: Roche entered an agreement to acquire PathAI, strengthening its position in AI-driven digital pathology, precision diagnostics, and companion diagnostic development.
• Tempus AI launched the ArteraAI Prostate Test, an AI-enabled digital pathology algorithm designed to support personalized prostate cancer treatment decisions.
• 2023-October: Pramana and Caris Life Sciences collaborated to digitize pathology slides at scale, supporting AI-based pathology analysis, biomarker identification, patient stratification, and precision oncology workflows.

List of Key Companies Profiled

• Tempus AI
• Caris Life Sciences
• Guardant Health, Inc.
• Roche
• ConcertAI
• SOPHiA GENETICS SA
• PathAI, Inc.
• Owkin, Inc.
• Recursion Pharmaceuticals, Inc.
• Personalis, Inc.

Global Artificial Intelligence In Precision Medicine Market Report Segmentation

By Component

• Software
• Services
• Hardware

By Technology

• Deep Learning
• Natural Language Processing
• Querying Method
• Context Aware Processing

By Therapeutic Application

• Oncology
• Cardiology
• Neurology
• Respiratory
• Other Therapeutic Application

By Geography

• North America
  • US
  • Canada
  • Mexico
  • Rest of North America
• Europe
  • Germany
  • UK
  • France
  • Russia
  • Spain
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Singapore
  • Malaysia
  • Rest of Asia Pacific
• LAMEA
  • Brazil
  • Argentina
  • UAE
  • Saudi Arabia
  • South Africa
  • Nigeria
  • Rest of LAMEA

Table of Contents

Chapter 1. Research Scope & Methodology

• 1.1 Market Definition
• 1.2 Analysis Period & Currency
• 1.3 Segmentation
• 1.4 Artificial Intelligence In Precision Medicine Market, by Geography
• 1.5 Research Methodology

Chapter 2. Market Overview

• 2.1 COVID-19 Impact
• 2.2 Market Composition and Scenario

Chapter 3. Key Factors Impacting Market

• 3.1 Market Drivers
• 3.2 Market Restraints
• 3.3 Market Opportunities
• 3.4 Market Challenges
• 3.5 Market Trends
• 3.6 State of Competition
• 3.7 Market Consolidation
• 3.8 Key Customer Criteria

Chapter 4. Product Life Cycle

Chapter 5. Value Chain Analysis of Artificial Intelligence in Precision Medicine Market

Chapter 6. Competition Analysis - Global

• 6.1 Market Share Analysis
• 6.2 Recent Developments
  • 6.2.1 Mergers & Acquisitions
  • 6.2.2 Product Launch & Product Expansion
  • 6.2.3 Partnership, Collaboration & Agreements
  • 6.2.4 Geographical Expansion

Chapter 7. Segmentation By Component

• 7.1 Software
• 7.2 Services
• 7.3 Hardware

Chapter 8. Segmentation By Technology

• 8.1 Deep Learning
• 8.2 Natural Language Processing
• 8.3 Querying Method
• 8.4 Context Aware Processing

Chapter 9. Segmentation By Therapeutic Application

• 9.1 Oncology
• 9.2 Cardiology
• 9.3 Neurology
• 9.4 Respiratory
• 9.5 Other Therapeutic Applications

Chapter 10. North America Market

• 10.1 Market Overview
• 10.2 Key Factors Impacting Market
  • 10.2.1 Market Drivers
  • 10.2.2 Market Restraints
  • 10.2.3 Market Opportunities
  • 10.2.4 Market Challenges
  • 10.2.5 Market Trends
  • 10.2.6 State of Competition
  • 10.2.7 Market Consolidation
  • 10.2.8 Key Customer Criteria
• 10.3 Product Life Cycle
• 10.4 Segmentation By Component
  • 10.4.1 Software
  • 10.4.2 Services
  • 10.4.3 Hardware
• 10.5 Segmentation By Technology
  • 10.5.1 Deep Learning
  • 10.5.2 Natural Language Processing
  • 10.5.3 Querying Method
  • 10.5.4 Context-Aware Processing
• 10.6 Segmentation By Therapeutic Application
  • 10.6.1 Oncology
  • 10.6.2 Cardiology
  • 10.6.3 Neurology
  • 10.6.4 Respiratory
  • 10.6.5 Other Therapeutic Application
• 10.7 Segmentation By Country
  • 10.7.1 US
    • 10.7.1.1 Segmentation By Component
      • 10.7.1.1.1 Software
      • 10.7.1.1.2 Services
      • 10.7.1.1.3 Hardware
    • 10.7.1.2 Segmentation By Technology
      • 10.7.1.2.1 Deep Learning
      • 10.7.1.2.2 Natural Language Processing
      • 10.7.1.2.3 Querying Method
      • 10.7.1.2.4 Context Aware Processing
    • 10.7.1.3 Segmentation By Therapeutic Application
      • 10.7.1.3.1 Oncology
      • 10.7.1.3.2 Cardiology
      • 10.7.1.3.3 Neurology
      • 10.7.1.3.4 Respiratory
      • 10.7.1.3.5 Other Therapeutic Application
  • 10.7.2 Canada
    • 10.7.2.1 Segmentation By Component
      • 10.7.2.1.1 Software
      • 10.7.2.1.2 Services
      • 10.7.2.1.3 Hardware
    • 10.7.2.2 Segmentation By Technology
      • 10.7.2.2.1 Deep Learning
      • 10.7.2.2.2 Natural Language Processing
      • 10.7.2.2.3 Querying Method
      • 10.7.2.2.4 Context Aware Processing
    • 10.7.2.3 Segmentation By Therapeutic Application
      • 10.7.2.3.1 Oncology
      • 10.7.2.3.2 Cardiology
      • 10.7.2.3.3 Neurology
      • 10.7.2.3.4 Respiratory
      • 10.7.2.3.5 Other Therapeutic Application
  • 10.7.3 Mexico
    • 10.7.3.1 Segmentation By Component
      • 10.7.3.1.1 Software
      • 10.7.3.1.2 Services
      • 10.7.3.1.3 Hardware
    • 10.7.3.2 Segmentation By Technology
      • 10.7.3.2.1 Deep Learning
      • 10.7.3.2.2 Natural Language Processing
      • 10.7.3.2.3 Querying Method
      • 10.7.3.2.4 Context Aware Processing
    • 10.7.3.3 Segmentation By Therapeutic Application
      • 10.7.3.3.1 Oncology
      • 10.7.3.3.2 Cardiology
      • 10.7.3.3.3 Neurology
      • 10.7.3.3.4 Respiratory
      • 10.7.3.3.5 Other Therapeutic Application
  • 10.7.4 Rest of North America
    • 10.7.4.1 Segmentation By Component
      • 10.7.4.1.1 Software
      • 10.7.4.1.2 Services
      • 10.7.4.1.3 Hardware
    • 10.7.4.2 Segmentation By Technology
      • 10.7.4.2.1 Deep Learning
      • 10.7.4.2.2 Natural Language Processing
      • 10.7.4.2.3 Querying Method
      • 10.7.4.2.4 Context Aware Processing
    • 10.7.4.3 Segmentation By Therapeutic Application
      • 10.7.4.3.1 Oncology
      • 10.7.4.3.2 Cardiology
      • 10.7.4.3.3 Neurology
      • 10.7.4.3.4 Respiratory
      • 10.7.4.3.5 Other Therapeutic Application

Chapter 11. Europe Market

• 11.1 Market Overview
• 11.2 Key Factors Impacting Market
  • 11.2.1 Market Drivers
  • 11.2.2 Market Restraints
  • 11.2.3 Market Opportunities
  • 11.2.4 Market Challenges
  • 11.2.5 Market Trends
  • 11.2.6 State of Competition
  • 11.2.7 Market Consolidation
  • 11.2.8 Key Customer Criteria
• 11.3 Product Life Cycle
• 11.4 Segmentation By Component
  • 11.4.1 Software
  • 11.4.2 Services
  • 11.4.3 Hardware
• 11.5 Segmentation By Technology
  • 11.5.1 Deep Learning
  • 11.5.2 Natural Language Processing
  • 11.5.3 Querying Method
  • 11.5.4 Context Aware Processing
• 11.6 Segmentation By Therapeutic Application
  • 11.6.1 Oncology
  • 11.6.2 Cardiology
  • 11.6.3 Neurology
  • 11.6.4 Respiratory
  • 11.6.5 Other Therapeutic Applications
• 11.7 Segmentation By Country
  • 11.7.1 Germany
    • 11.7.1.1 Segmentation By Component
      • 11.7.1.1.1 Software
      • 11.7.1.1.2 Services
      • 11.7.1.1.3 Hardware
    • 11.7.1.2 Segmentation By Technology
      • 11.7.1.2.1 Deep Learning
      • 11.7.1.2.2 Natural Language Processing
      • 11.7.1.2.3 Querying Method
      • 11.7.1.2.4 Context Aware Processing
    • 11.7.1.3 Segmentation By Therapeutic Application
      • 11.7.1.3.1 Oncology
      • 11.7.1.3.2 Cardiology
      • 11.7.1.3.3 Neurology
      • 11.7.1.3.4 Respiratory
      • 11.7.1.3.5 Other Therapeutic Application
  • 11.7.2 UK
    • 11.7.2.1 Segmentation By Component
      • 11.7.2.1.1 Software
      • 11.7.2.1.2 Services
      • 11.7.2.1.3 Hardware
    • 11.7.2.2 Segmentation By Technology
      • 11.7.2.2.1 Deep Learning
      • 11.7.2.2.2 Natural Language Processing
      • 11.7.2.2.3 Querying Method
      • 11.7.2.2.4 Context Aware Processing
    • 11.7.2.3 Segmentation By Therapeutic Application
      • 11.7.2.3.1 Oncology
      • 11.7.2.3.2 Cardiology
      • 11.7.2.3.3 Neurology
      • 11.7.2.3.4 Respiratory
      • 11.7.2.3.5 Other Therapeutic Application
  • 11.7.3 France
    • 11.7.3.1 Segmentation By Component
      • 11.7.3.1.1 Software
      • 11.7.3.1.2 Services
      • 11.7.3.1.3 Hardware
    • 11.7.3.2 Segmentation By Technology
      • 11.7.3.2.1 Deep Learning
      • 11.7.3.2.2 Natural Language Processing
      • 11.7.3.2.3 Querying Method
      • 11.7.3.2.4 Context Aware Processing
    • 11.7.3.3 Segmentation By Therapeutic Application
      • 11.7.3.3.1 Oncology
      • 11.7.3.3.2 Cardiology
      • 11.7.3.3.3 Neurology
      • 11.7.3.3.4 Respiratory
      • 11.7.3.3.5 Other Therapeutic Application
  • 11.7.4 Russia
    • 11.7.4.1 Segmentation By Component
      • 11.7.4.1.1 Software
      • 11.7.4.1.2 Services
      • 11.7.4.1.3 Hardware
    • 11.7.4.2 Segmentation By Technology
      • 11.7.4.2.1 Deep Learning
      • 11.7.4.2.2 Natural Language Processing
      • 11.7.4.2.3 Querying Method
      • 11.7.4.2.4 Context Aware Processing
    • 11.7.4.3 Segmentation By Therapeutic Application
      • 11.7.4.3.1 Oncology
      • 11.7.4.3.2 Cardiology
      • 11.7.4.3.3 Neurology
      • 11.7.4.3.4 Respiratory
      • 11.7.4.3.5 Other Therapeutic Application
  • 11.7.5 Spain
    • 11.7.5.1 Segmentation By Component
      • 11.7.5.1.1 Software
      • 11.7.5.1.2 Services
      • 11.7.5.1.3 Hardware
    • 11.7.5.2 Segmentation By Technology
      • 11.7.5.2.1 Deep Learning
      • 11.7.5.2.2 Natural Language Processing
      • 11.7.5.2.3 Querying Method
      • 11.7.5.2.4 Context Aware Processing
    • 11.7.5.3 Segmentation By Therapeutic Application
      • 11.7.5.3.1 Oncology
      • 11.7.5.3.2 Cardiology
      • 11.7.5.3.3 Neurology
      • 11.7.5.3.4 Respiratory
      • 11.7.5.3.5 Other Therapeutic Application
  • 11.7.6 Italy
    • 11.7.6.1 Segmentation By Component
      • 11.7.6.1.1 Software
      • 11.7.6.1.2 Services
      • 11.7.6.1.3 Hardware
    • 11.7.6.2 Segmentation By Technology
      • 11.7.6.2.1 Deep Learning
      • 11.7.6.2.2 Natural Language Processing
      • 11.7.6.2.3 Querying Method
      • 11.7.6.2.4 Context Aware Processing
    • 11.7.6.3 Segmentation By Therapeutic Application
      • 11.7.6.3.1 Oncology
      • 11.7.6.3.2 Cardiology
      • 11.7.6.3.3 Neurology
      • 11.7.6.3.4 Respiratory
      • 11.7.6.3.5 Other Therapeutic Application
  • 11.7.7 Rest of Europe
    • 11.7.7.1 Segmentation By Component
      • 11.7.7.1.1 Software
      • 11.7.7.1.2 Services
      • 11.7.7.1.3 Hardware
    • 11.7.7.2 Segmentation By Technology
      • 11.7.7.2.1 Deep Learning
      • 11.7.7.2.2 Natural Language Processing
      • 11.7.7.2.3 Querying Method
      • 11.7.7.2.4 Context Aware Processing
    • 11.7.7.3 Segmentation By Therapeutic Application
      • 11.7.7.3.1 Oncology
      • 11.7.7.3.2 Cardiology
      • 11.7.7.3.3 Neurology
      • 11.7.7.3.4 Respiratory
      • 11.7.7.3.5 Other Therapeutic Application

Chapter 12. Asia Pacific Market

• 12.1 Market Overview
• 12.2 Key Factors Impacting Market
  • 12.2.1 Market Drivers
  • 12.2.2 Market Restraints
  • 12.2.3 Market Opportunities
  • 12.2.4 Market Challenges
  • 12.2.5 Market Trends
  • 12.2.6 State of Competition
  • 12.2.7 Market Consolidation
  • 12.2.8 Key Customer Criteria
• 12.3 Product Life Cycle
• 12.4 Segmentation By Component
  • 12.4.1 Software
  • 12.4.2 Services
  • 12.4.3 Hardware
• 12.5 Segmentation By Technology
  • 12.5.1 Deep Learning
  • 12.5.2 Natural Language Processing (NLP)
  • 12.5.3 Querying Method
  • 12.5.4 Context Aware Processing
• 12.6 Segmentation By Therapeutic Application
  • 12.6.1 Oncology
  • 12.6.2 Cardiology
  • 12.6.3 Neurology
  • 12.6.4 Respiratory
  • 12.6.5 Other Therapeutic Application
• 12.7 Segmentation By Country
  • 12.7.1 China
    • 12.7.1.1 Segmentation By Component
      • 12.7.1.1.1 Software
      • 12.7.1.1.2 Services
      • 12.7.1.1.3 Hardware
    • 12.7.1.2 Segmentation By Technology
      • 12.7.1.2.1 Deep Learning
      • 12.7.1.2.2 Natural Language Processing
      • 12.7.1.2.3 Querying Method
      • 12.7.1.2.4 Context Aware Processing
    • 12.7.1.3 Segmentation By Therapeutic Application
      • 12.7.1.3.1 Oncology
      • 12.7.1.3.2 Cardiology
      • 12.7.1.3.3 Neurology
      • 12.7.1.3.4 Respiratory
      • 12.7.1.3.5 Other Therapeutic Application
  • 12.7.2 Japan
    • 12.7.2.1 Segmentation By Component
      • 12.7.2.1.1 Software
      • 12.7.2.1.2 Services
      • 12.7.2.1.3 Hardware
    • 12.7.2.2 Segmentation By Technology
      • 12.7.2.2.1 Deep Learning
      • 12.7.2.2.2 Natural Language Processing
      • 12.7.2.2.3 Querying Method
      • 12.7.2.2.4 Context Aware Processing
    • 12.7.2.3 Segmentation By Therapeutic Application
      • 12.7.2.3.1 Oncology
      • 12.7.2.3.2 Cardiology
      • 12.7.2.3.3 Neurology
      • 12.7.2.3.4 Respiratory
      • 12.7.2.3.5 Other Therapeutic Application
  • 12.7.3 India
    • 12.7.3.1 Segmentation By Component
      • 12.7.3.1.1 Software
      • 12.7.3.1.2 Services
      • 12.7.3.1.3 Hardware
    • 12.7.3.2 Segmentation By Technology
      • 12.7.3.2.1 Deep Learning
      • 12.7.3.2.2 Natural Language Processing
      • 12.7.3.2.3 Querying Method
      • 12.7.3.2.4 Context Aware Processing
    • 12.7.3.3 Segmentation By Therapeutic Application
      • 12.7.3.3.1 Oncology
      • 12.7.3.3.2 Cardiology
      • 12.7.3.3.3 Neurology
      • 12.7.3.3.4 Respiratory
      • 12.7.3.3.5 Other Therapeutic Application
  • 12.7.4 South Korea
    • 12.7.4.1 Segmentation By Component
      • 12.7.4.1.1 Software
      • 12.7.4.1.2 Services
      • 12.7.4.1.3 Hardware
    • 12.7.4.2 Segmentation By Technology
      • 12.7.4.2.1 Deep Learning
      • 12.7.4.2.2 Natural Language Processing
      • 12.7.4.2.3 Querying Method
      • 12.7.4.2.4 Context Aware Processing
    • 12.7.4.3 Segmentation By Therapeutic Application
      • 12.7.4.3.1 Oncology
      • 12.7.4.3.2 Cardiology
      • 12.7.4.3.3 Neurology
      • 12.7.4.3.4 Respiratory
      • 12.7.4.3.5 Other Therapeutic Application
  • 12.7.5 Singapore
    • 12.7.5.1 Segmentation By Component
      • 12.7.5.1.1 Software
      • 12.7.5.1.2 Services
      • 12.7.5.1.3 Hardware
    • 12.7.5.2 Segmentation By Technology
      • 12.7.5.2.1 Deep Learning
      • 12.7.5.2.2 Natural Language Processing
      • 12.7.5.2.3 Querying Method
      • 12.7.5.2.4 Context Aware Processing
    • 12.7.5.3 Segmentation By Therapeutic Application
      • 12.7.5.3.1 Oncology
      • 12.7.5.3.2 Cardiology
      • 12.7.5.3.3 Neurology
      • 12.7.5.3.4 Respiratory
      • 12.7.5.3.5 Other Therapeutic Application
  • 12.7.6 Malaysia
    • 12.7.6.1 Segmentation By Component
      • 12.7.6.1.1 Software
      • 12.7.6.1.2 Services
      • 12.7.6.1.3 Hardware
    • 12.7.6.2 Segmentation By Technology
      • 12.7.6.2.1 Deep Learning
      • 12.7.6.2.2 Natural Language Processing
      • 12.7.6.2.3 Querying Method
      • 12.7.6.2.4 Context Aware Processing
    • 12.7.6.3 Segmentation By Therapeutic Application
      • 12.7.6.3.1 Oncology
      • 12.7.6.3.2 Cardiology
      • 12.7.6.3.3 Neurology
      • 12.7.6.3.4 Respiratory
      • 12.7.6.3.5 Other Therapeutic Application
  • 12.7.7 Rest of Asia Pacific
    • 12.7.7.1 Segmentation By Component
      • 12.7.7.1.1 Software
      • 12.7.7.1.2 Services
      • 12.7.7.1.3 Hardware
    • 12.7.7.2 Segmentation By Technology
      • 12.7.7.2.1 Deep Learning
      • 12.7.7.2.2 Natural Language Processing
      • 12.7.7.2.3 Querying Method
      • 12.7.7.2.4 Context Aware Processing
    • 12.7.7.3 Segmentation By Therapeutic Application
      • 12.7.7.3.1 Oncology
      • 12.7.7.3.2 Cardiology
      • 12.7.7.3.3 Neurology
      • 12.7.7.3.4 Respiratory
      • 12.7.7.3.5 Other Therapeutic Application

Chapter 13. LAMEA Market

• 13.1 Market Overview
• 13.2 Key Factors Impacting Market
  • 13.2.1 Market Drivers
  • 13.2.2 Market Restraints
  • 13.2.3 Market Opportunities
  • 13.2.4 Market Challenges
  • 13.2.5 Market Trends
  • 13.2.6 State of Competition
  • 13.2.7 Market Consolidation
  • 13.2.8 Key Customer Criteria
• 13.3 Product Life Cycle
• 13.4 Segmentation By Component
  • 13.4.1 Software
  • 13.4.2 Services
  • 13.4.3 Hardware
• 13.5 Segmentation By Technology
  • 13.5.1 Deep Learning
  • 13.5.2 Natural Language Processing
  • 13.5.3 Querying Method
  • 13.5.4 Context Aware Processing
• 13.6 Segmentation By Therapeutic Application
  • 13.6.1 Oncology
  • 13.6.2 Cardiology
  • 13.6.3 Neurology
  • 13.6.4 Respiratory
  • 13.6.5 Other Therapeutic Application
• 13.7 Segmentation By Country
  • 13.7.1 Brazil
    • 13.7.1.1 Segmentation By Component
      • 13.7.1.1.1 Software
      • 13.7.1.1.2 Services
      • 13.7.1.1.3 Hardware
    • 13.7.1.2 Segmentation By Technology
      • 13.7.1.2.1 Deep Learning
      • 13.7.1.2.2 Natural Language Processing
      • 13.7.1.2.3 Querying Method
      • 13.7.1.2.4 Context Aware Processing
    • 13.7.1.3 Segmentation By Therapeutic Application
      • 13.7.1.3.1 Oncology
      • 13.7.1.3.2 Cardiology
      • 13.7.1.3.3 Neurology
      • 13.7.1.3.4 Respiratory
      • 13.7.1.3.5 Other Therapeutic Application
  • 13.7.2 Argentina
    • 13.7.2.1 Segmentation By Component
      • 13.7.2.1.1 Software
      • 13.7.2.1.2 Services
      • 13.7.2.1.3 Hardware
    • 13.7.2.2 Segmentation By Technology
      • 13.7.2.2.1 Deep Learning
      • 13.7.2.2.2 Natural Language Processing
      • 13.7.2.2.3 Querying Method
      • 13.7.2.2.4 Context Aware Processing
    • 13.7.2.3 Segmentation By Therapeutic Application
      • 13.7.2.3.1 Oncology
      • 13.7.2.3.2 Cardiology
      • 13.7.2.3.3 Neurology
      • 13.7.2.3.4 Respiratory
      • 13.7.2.3.5 Other Therapeutic Application
  • 13.7.3 UAE
    • 13.7.3.1 Segmentation By Component
      • 13.7.3.1.1 Software
      • 13.7.3.1.2 Services
      • 13.7.3.1.3 Hardware
    • 13.7.3.2 Segmentation By Technology
      • 13.7.3.2.1 Deep Learning
      • 13.7.3.2.2 Natural Language Processing
      • 13.7.3.2.3 Querying Method
      • 13.7.3.2.4 Context Aware Processing
    • 13.7.3.3 Segmentation By Therapeutic Application
      • 13.7.3.3.1 Oncology
      • 13.7.3.3.2 Cardiology
      • 13.7.3.3.3 Neurology
      • 13.7.3.3.4 Respiratory
      • 13.7.3.3.5 Other Therapeutic Application
  • 13.7.4 Saudi Arabia
    • 13.7.4.1 Segmentation By Component
      • 13.7.4.1.1 Software
      • 13.7.4.1.2 Services
      • 13.7.4.1.3 Hardware
    • 13.7.4.2 Segmentation By Technology
      • 13.7.4.2.1 Deep Learning
      • 13.7.4.2.2 Natural Language Processing
      • 13.7.4.2.3 Querying Method
      • 13.7.4.2.4 Context Aware Processing
    • 13.7.4.3 Segmentation By Therapeutic Application
      • 13.7.4.3.1 Oncology
      • 13.7.4.3.2 Cardiology
      • 13.7.4.3.3 Neurology
      • 13.7.4.3.4 Respiratory
      • 13.7.4.3.5 Other Therapeutic Application
  • 13.7.5 South Africa
    • 13.7.5.1 Segmentation By Component
      • 13.7.5.1.1 Software
      • 13.7.5.1.2 Services
      • 13.7.5.1.3 Hardware
    • 13.7.5.2 Segmentation By Technology
      • 13.7.5.2.1 Deep Learning
      • 13.7.5.2.2 Natural Language Processing
      • 13.7.5.2.3 Querying Method
      • 13.7.5.2.4 Context Aware Processing
    • 13.7.5.3 Segmentation By Therapeutic Application
      • 13.7.5.3.1 Oncology
      • 13.7.5.3.2 Cardiology
      • 13.7.5.3.3 Neurology
      • 13.7.5.3.4 Respiratory
      • 13.7.5.3.5 Other Therapeutic Application
  • 13.7.6 Nigeria
    • 13.7.6.1 Segmentation By Component
      • 13.7.6.1.1 Software
      • 13.7.6.1.2 Services
      • 13.7.6.1.3 Hardware
    • 13.7.6.2 Segmentation By Technology
      • 13.7.6.2.1 Deep Learning
      • 13.7.6.2.2 Natural Language Processing
      • 13.7.6.2.3 Querying Method
      • 13.7.6.2.4 Context Aware Processing
    • 13.7.6.3 Segmentation By Therapeutic Application
      • 13.7.6.3.1 Oncology
      • 13.7.6.3.2 Cardiology
      • 13.7.6.3.3 Neurology
      • 13.7.6.3.4 Respiratory
      • 13.7.6.3.5 Other Therapeutic Application
  • 13.7.7 Rest of LAMEA
    • 13.7.7.1 Segmentation By Component
      • 13.7.7.1.1 Software
      • 13.7.7.1.2 Services
      • 13.7.7.1.3 Hardware
    • 13.7.7.2 Segmentation By Technology
      • 13.7.7.2.1 Deep Learning
      • 13.7.7.2.2 Natural Language Processing
      • 13.7.7.2.3 Querying Method
      • 13.7.7.2.4 Context Aware Processing
    • 13.7.7.3 Segmentation By Therapeutic Application
      • 13.7.7.3.1 Oncology
      • 13.7.7.3.2 Cardiology
      • 13.7.7.3.3 Neurology
      • 13.7.7.3.4 Respiratory
      • 13.7.7.3.5 Other Therapeutic Application

Chapter 14. Company Snapshots

• 14.1 Roche
  • 14.1.1 Business Overview
  • 14.1.2 Key Information
  • 14.1.3 Company Focus on Artificial Intelligence in Precision Medicine Market
  • 14.1.4 Strategic Insights
  • 14.1.5 Strategy Deployed
  • 14.1.6 Product & Service Portfolio
  • 14.1.7 SWOT Analysis
  • 14.1.8 Key Differentiators
• 14.2 ConcertAI
  • 14.2.1 Business Overview
  • 14.2.2 Key Information
  • 14.2.3 Company Focus on Artificial Intelligence in Precision Medicine Market
  • 14.2.4 Strategic Insights
  • 14.2.5 Portfolio Matrix
  • 14.2.6 SWOT Analysis
  • 14.2.7 Key Differentiators
• 14.3 SOPHiA GENETICS
  • 14.3.1 Business Overview
  • 14.3.2 Key Information
  • 14.3.3 Company Focus on AI in Precision Medicine
  • 14.3.4 Strategic Insights
  • 14.3.5 Portfolio Matrix
  • 14.3.6 SWOT Analysis
  • 14.3.7 Key Differentiators
• 14.4 PathAI
  • 14.4.1 Business Overview
  • 14.4.2 Key Information
  • 14.4.3 Company Focus on AI in Precision Medicine
  • 14.4.4 Strategic Insights
  • 14.4.5 Portfolio Matrix
  • 14.4.6 SWOT Analysis
  • 14.4.7 Key Differentiators
• 14.5 Owkin
  • 14.5.1 Business Overview
  • 14.5.2 Key Information
  • 14.5.3 Company Focus on AI in Precision Medicine
  • 14.5.4 Strategic Insights
  • 14.5.5 Portfolio Matrix
  • 14.5.6 SWOT Analysis
  • 14.5.7 Key Differentiators
• 14.6 Recursion
  • 14.6.1 Business Overview
  • 14.6.2 Key Information
  • 14.6.3 Company Focus on AI in Precision Medicine
  • 14.6.4 Strategic Insights
  • 14.6.5 Portfolio Matrix
  • 14.6.6 SWOT Analysis
  • 14.6.7 Key Differentiators
• 14.7 Personalis
  • 14.7.1 Business Overview
  • 14.7.2 Key Information
  • 14.7.3 Company Focus on AI in Precision Medicine
  • 14.7.4 Strategic Insights
  • 14.7.5 Strategy Deployed
  • 14.7.6 Portfolio Matrix
  • 14.7.7 SWOT Analysis
  • 14.7.8 Key Differentiators
• 14.8 Tempus AI
  • 14.8.1 Business Overview
  • 14.8.2 Key Information
  • 14.8.3 Company Focus on Artificial Intelligence in Precision Medicine Market
  • 14.8.4 Strategic Insights
  • 14.8.5 Strategy Deployed
  • 14.8.6 Product & Service Portfolio
  • 14.8.7 Technology & Innovation Focus
  • 14.8.8 SWOT Analysis
  • 14.8.9 Key Differentiators
  • 14.8.10 Portfolio Matrix
  • 14.8.11 Future Outlook
• 14.9 Caris Life Sciences
  • 14.9.1 Business Overview
  • 14.9.2 Key Information
  • 14.9.3 Company Focus on Artificial Intelligence in Precision Medicine Market
  • 14.9.4 Strategic Insights
  • 14.9.5 Strategy Deployed
  • 14.9.6 Product & Service Portfolio
  • 14.9.7 Technology & Innovation Focus
  • 14.9.8 SWOT Analysis
  • 14.9.9 Key Differentiators
  • 14.9.10 Portfolio Matrix
  • 14.9.11 Future Outlook
• 14.10 Guardant Health
  • 14.10.1 Business Overview
  • 14.10.2 Key Information
  • 14.10.3 Company Focus on Artificial Intelligence in Precision Medicine Market
  • 14.10.4 Strategic Insights
  • 14.10.5 Strategy Deployed
  • 14.10.6 Product & Service Portfolio
  • 14.10.7 Technology & Innovation Focus
  • 14.10.8 SWOT Analysis
  • 14.10.9 Key Differentiators
  • 14.10.10 Portfolio Matrix
  • 14.10.11 Future Outlook
  • 14.10.12 Analyst View

Chapter 15. Winning Imperatives of Artificial Intelligence In Precision Medicine Market