AI 기반 진단 및 예측 의료 시장 예측(-2032년) : 구성요소별, 질환 영역별, 데이터 유형별, 기술별, 용도별, 최종사용자별, 지역별 분석

According to Stratistics MRC, the Global AI Driven Diagnostics & Predictive Care Market is accounted for $1.94 billion in 2025 and is expected to reach $8.01 billion by 2032 growing at a CAGR of 22.4% during the forecast period. AI Driven Diagnostics & Predictive Care refers to the use of artificial intelligence, machine learning, and advanced data analytics to improve disease detection, diagnosis, and outcome prediction. By analyzing vast volumes of clinical records, medical imaging, genomics, and real-time patient data, AI systems identify patterns that support early diagnosis and personalized treatment planning. Predictive care focuses on anticipating health risks before symptoms escalate, enabling preventive interventions. Grounded in traditional medical practice and strengthened by digital innovation, this approach enhances clinical accuracy, reduces healthcare costs, improves patient outcomes, and supports a proactive, data-driven healthcare ecosystem.

Market Dynamics:

Driver:

Big Data & Advanced Analytics

Big data and advanced analytics are the central drivers of the AI Driven Diagnostics & Predictive Care Market. The growing availability of electronic health records, medical imaging databases, genomic data, and real-time patient monitoring creates fertile ground for AI algorithms. Advanced analytics enable faster pattern recognition, risk stratification, and clinical decision support. These capabilities improve diagnostic precision, reduce human error, and allow early disease detection, accelerating adoption across hospitals, diagnostic centers, and research institutions worldwide.

Restraint:

Reluctance among Medical Practitioners

Reluctance among medical practitioners remains a key restraint to market growth. Many clinicians are cautious about relying on AI-driven insights due to concerns over accuracy, transparency, and accountability in clinical decision-making. Limited understanding of AI systems, fear of workflow disruption, and resistance to changing long-established diagnostic practices slows adoption. Additionally, lack of adequate training and explainability in AI models creates trust gaps, particularly in high-stakes diagnostic and predictive care applications.

Opportunity:

Government Initiatives & Funding

Government initiatives and funding present's strong growth opportunities for the market. Public investments in digital health infrastructure, AI research, and healthcare modernization are accelerating adoption of AI-driven diagnostics. National strategies promoting early disease detection, precision medicine, and data-driven healthcare encourage hospitals and research institutions to deploy predictive care solutions. Grants, pilot programs, and regulatory sandboxes further support innovation, enabling startups and established players to scale AI technologies across public healthcare systems.

Threat:

Regulatory Uncertainty

Regulatory uncertainty poses a significant threat to the market. Regulations governing AI validation, data usage, clinical liability, and algorithm transparency vary widely across regions. Frequent regulatory changes increase compliance costs and delay product approvals. Unclear guidelines on accountability in AI-assisted diagnosis create legal risks for providers and developers. This uncertainty can discourage investment, slow innovation, and restrict cross-border deployment of AI-driven diagnostic solutions.

Covid-19 Impact:

The COVID-19 pandemic positively influenced the AI Driven Diagnostics & Predictive Care Market by accelerating adoption of AI tools for rapid diagnosis, disease progression prediction, and patient triage. AI-powered imaging analysis and predictive models supported overwhelmed healthcare systems. However, the crisis also exposed data quality gaps and infrastructure limitations. Overall, COVID-19 strengthened acceptance of AI in clinical settings and reinforced the importance of predictive analytics for future healthcare resilience.

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

The oncology segment is expected to account for the largest market share during the forecast period, due to the high complexity, data intensity, and critical need for early cancer detection. AI-driven diagnostics enhance tumor identification, treatment response prediction, and personalized therapy selection using imaging and genomic data. Rising global cancer prevalence and increasing adoption of precision oncology solutions further drive demand, making oncology the most prominent application area for AI-driven diagnostics and predictive care.

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

Over the forecast period, the diagnostic imaging segment is predicted to witness the highest growth rate, due to increasing adoption of AI-powered imaging solutions for precise and early disease detection. Advanced machine learning algorithms enhance interpretation of X-rays, CT scans, and MRIs, reducing diagnostic errors and improving workflow efficiency. Rising demand for non-invasive, rapid diagnostic techniques, coupled with the integration of predictive analytics, fuels growth. Technological innovations in AI-enabled imaging platforms and cloud-based solutions further accelerate adoption globally.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, due to expanding healthcare infrastructure, growing digital health adoption, and increasing government investments in AI-driven diagnostics. Rising prevalence of chronic diseases, expanding hospital networks, and a surge in medical imaging facilities support market growth. Additionally, public and private sector initiatives to modernize healthcare systems and deploy predictive care solutions accelerate adoption. The region's cost-effective healthcare services and large patient population make it a prime market for AI integration.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to widespread electronic health record adoption, and significant AI research investments. High awareness of precision medicine, increasing integration of predictive analytics, and strong funding support drive rapid deployment of AI-driven diagnostics. The presence of leading technology providers and extensive clinical data availability further fuels growth. North America's focus on innovation, early disease detection, and personalized care ensures accelerated adoption of AI solutions across hospitals and research institutions.

Key players in the market

Some of the key players in AI Driven Diagnostics & Predictive Care Market include Siemens Healthineers, Butterfly Network, GE HealthCare, Enlitic, Aidoc, Lunit, IBM Watson Health, Arterys, Philips Healthcare, Paige AI, Zebra Medical Vision, Qure.ai, Tempus, Freenome, and PathAI.

Key Developments:

In November 2025, Siemens Healthineers introduced Syngo Carbon 2.0, an upgraded enterprise imaging platform. The launch integrates multimodal imaging data, AI-powered workflow automation, and cloud-based collaboration, designed to streamline radiology operations and improve diagnostic accuracy across global healthcare systems.

In October 2025, Siemens Healthineers expanded its collaboration with Varian and multiple oncology centers to accelerate precision therapy solutions. The joint venture integrates imaging, radiation therapy, and AI-driven planning tools, aiming to improve cancer treatment outcomes and strengthen Siemens' leadership in oncology care.

Components Covered:

• Software
• Hardware
• Services

Disease Areas Covered:

• Oncology
• Cardiovascular Diseases
• Neurological Disorders
• Respiratory Diseases
• Diabetes & Metabolic Disorders
• Infectious Diseases
• Rare Diseases

Data Types Covered:

• Medical Imaging Data
• Electronic Health Records (EHR)
• Genomic & Omics Data
• Wearable & Remote Monitoring Data
• Clinical Notes & Unstructured Data

Technologies Covered:

• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Generative AI
• Hybrid AI Models

Applications Covered:

• Diagnostic Imaging
• Predictive Care
• Clinical Workflow Optimization
• Population Health Management

End Users Covered:

• Hospitals & Clinics
• Diagnostic Imaging Centers
• Ambulatory Care Centers
• Academic & Research Institutes
• Healthcare Payers
• Pharmaceutical & Biotechnology Companies

Regions Covered:

• North America
  • US
  • Canada
  • Mexico
• Europe
  • Germany
  • UK
  • Italy
  • France
  • Spain
  • Rest of Europe
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • New Zealand
  • South Korea
  • Rest of Asia Pacific
• South America
  • Argentina
  • Brazil
  • Chile
  • Rest of South America
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Qatar
  • South Africa
  • Rest of Middle East & 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 2024, 2025, 2026, 2028, and 2032
• 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

2 Preface

• 2.1 Abstract
• 2.2 Stake Holders
• 2.3 Research Scope
• 2.4 Research Methodology
  • 2.4.1 Data Mining
  • 2.4.2 Data Analysis
  • 2.4.3 Data Validation
  • 2.4.4 Research Approach
• 2.5 Research Sources
  • 2.5.1 Primary Research Sources
  • 2.5.2 Secondary Research Sources
  • 2.5.3 Assumptions

3 Market Trend Analysis

• 3.1 Introduction
• 3.2 Drivers
• 3.3 Restraints
• 3.4 Opportunities
• 3.5 Threats
• 3.6 Technology Analysis
• 3.7 Application Analysis
• 3.8 End User Analysis
• 3.9 Emerging Markets
• 3.10 Impact of Covid-19

4 Porters Five Force Analysis

• 4.1 Bargaining power of suppliers
• 4.2 Bargaining power of buyers
• 4.3 Threat of substitutes
• 4.4 Threat of new entrants
• 4.5 Competitive rivalry

5 Global AI Driven Diagnostics & Predictive Care Market, By Component

• 5.1 Introduction
• 5.2 Software
• 5.3 Hardware
  • 5.3.1 AI-Enabled Imaging Systems
  • 5.3.2 Wearable & IoT Devices
  • 5.3.3 Edge AI Devices
• 5.4 Services
  • 5.4.1 Implementation & Integration
  • 5.4.2 Training & Consulting
  • 5.4.3 Support & Maintenance

6 Global AI Driven Diagnostics & Predictive Care Market, By Disease Area

• 6.1 Introduction
• 6.2 Oncology
• 6.3 Cardiovascular Diseases
• 6.4 Neurological Disorders
• 6.5 Respiratory Diseases
• 6.6 Diabetes & Metabolic Disorders
• 6.7 Infectious Diseases
• 6.8 Rare Diseases

7 Global AI Driven Diagnostics & Predictive Care Market, By Data Type

• 7.1 Introduction
• 7.2 Medical Imaging Data
• 7.3 Electronic Health Records (EHR)
• 7.4 Genomic & Omics Data
• 7.5 Wearable & Remote Monitoring Data
• 7.6 Clinical Notes & Unstructured Data

8 Global AI Driven Diagnostics & Predictive Care Market, By Technology

• 8.1 Introduction
• 8.2 Machine Learning
• 8.3 Deep Learning
• 8.4 Natural Language Processing (NLP)
• 8.5 Computer Vision
• 8.6 Generative AI
• 8.7 Hybrid AI Models

9 Global AI Driven Diagnostics & Predictive Care Market, By Application

• 9.1 Introduction
• 9.2 Diagnostic Imaging
• 9.3 Predictive Care
  • 9.3.1 Disease Risk Prediction
  • 9.3.2 Patient Deterioration Prediction
  • 9.3.3 Hospital Readmission Prediction
  • 9.3.4 Treatment Outcome Prediction
• 9.4 Clinical Workflow Optimization
• 9.5 Population Health Management

10 Global AI Driven Diagnostics & Predictive Care Market, By End User

• 10.1 Introduction
• 10.2 Hospitals & Clinics
• 10.3 Diagnostic Imaging Centers
• 10.4 Ambulatory Care Centers
• 10.5 Academic & Research Institutes
• 10.6 Healthcare Payers
• 10.7 Pharmaceutical & Biotechnology Companies

11 Global AI Driven Diagnostics & Predictive Care Market, By Geography

• 11.1 Introduction
• 11.2 North America
  • 11.2.1 US
  • 11.2.2 Canada
  • 11.2.3 Mexico
• 11.3 Europe
  • 11.3.1 Germany
  • 11.3.2 UK
  • 11.3.3 Italy
  • 11.3.4 France
  • 11.3.5 Spain
  • 11.3.6 Rest of Europe
• 11.4 Asia Pacific
  • 11.4.1 Japan
  • 11.4.2 China
  • 11.4.3 India
  • 11.4.4 Australia
  • 11.4.5 New Zealand
  • 11.4.6 South Korea
  • 11.4.7 Rest of Asia Pacific
• 11.5 South America
  • 11.5.1 Argentina
  • 11.5.2 Brazil
  • 11.5.3 Chile
  • 11.5.4 Rest of South America
• 11.6 Middle East & Africa
  • 11.6.1 Saudi Arabia
  • 11.6.2 UAE
  • 11.6.3 Qatar
  • 11.6.4 South Africa
  • 11.6.5 Rest of Middle East & Africa

12 Key Developments

• 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
• 12.2 Acquisitions & Mergers
• 12.3 New Product Launch
• 12.4 Expansions
• 12.5 Other Key Strategies

13 Company Profiling

• 13.1 Siemens Healthineers
• 13.2 Butterfly Network
• 13.3 GE HealthCare
• 13.4 Enlitic
• 13.5 Aidoc
• 13.6 Lunit
• 13.7 IBM Watson Health
• 13.8 Arterys
• 13.9 Philips Healthcare
• 13.10 Paige AI
• 13.11 Zebra Medical Vision
• 13.12 Qure.ai
• 13.13 Tempus
• 13.14 Freenome
• 13.15 PathAI