임상 의사결정 지원 분야 인공지능 시장 규모, 점유율, 동향 분석 보고서 : 구성요소별, 용도별, 도입 형태별, 최종 용도별, 지역별 및 부문별 예측(2026-2033년)

Artificial Intelligence In Clinical Decision Support Market Summary

The global artificial intelligence in clinical decision support market size was estimated at USD 1.3 billion in 2025 and is projected to reach USD 4.5 billion in 2033, growing at a CAGR of 17.1% from 2026 to 2033. The increasing adoption of Electronic Health Records (EHRs), the shift toward value-based care models, and the growing demand for clinical workflow efficiency are driving market growth.

The section below outlines the key factors driving the growth of the artificial intelligence (AI) in clinical decision support industry, highlighting the increasing adoption of Electronic Health Records (EHRs), the increasing shift toward value-based care models, and the growing demand for clinical workflow efficiency.

Market Drivers and Dynamics

Increasing Adoption of Electronic Health Records (EHRs)

The increasing adoption of Electronic Health Records (EHRs) is essential for AI in Clinical Decision Support, as these platforms rely on large volumes of digitized patient data to generate actionable insights. Healthcare systems across the U.S., Europe, and parts of the Asia Pacific have achieved high levels of EHR penetration, supported by regulatory mandates and incentive programs. This widespread digitization allows AI-CDSS solutions to access longitudinal patient records, including medical history, lab results, imaging data, and medication profiles. Seamless integration of CDSS within EHR workflows enables real-time clinical recommendations at the point of care, improving diagnostic accuracy and treatment decisions. For instance, Oracle's EHR-integrated CDSS prototype uses Oracle Cloud Infrastructure (OCI) AI Vision to analyze patient images for precise skin cancer detection.

Increasing Shift toward Value-Based Care Models

The shift toward value-based care models is transforming healthcare delivery by linking reimbursement to patient outcomes, clinical quality, and cost control. AI-CDSS strengthens care coordination and ongoing patient management. By analyzing real-time and historical patient data, these systems pinpoint high-risk individuals, predict readmission risk, and support timely interventions. Integration with EHRs and population health platforms enables providers to track outcomes, ensure adherence to clinical guidelines, and streamline resource use. This facilitates proactive care, especially in chronic disease management, where continuous monitoring and timely changes are vital. For example, a BMJ study in March 2026 analyzed an AI-powered clinical decision support system across 40 Chinese hospitals, randomizing 21,603 patients with acute ischemic stroke to AI-assisted or standard care. AI CDSS assessed data, including demographics, imaging, labs, and vitals, to generate tailored guideline recommendations. The study showed that the stroke CDSS reduced new vascular events at three months, improved stroke care quality, and lowered long-term vascular event rates.

Growing Demand for Clinical Workflow Efficiency

Rising patient volumes, workforce shortages, and increasing administrative burdens across healthcare systems are driving the growing demand for clinical workflow efficiency. Clinicians spend a significant portion of their time on documentation, data retrieval, and care coordination, which limits direct patient interaction and contributes to burnout. AI in Clinical Decision Support addresses these challenges by automating routine tasks and embedding decision support within existing clinical workflows. AI-CDSS also enhances interdisciplinary coordination and operational efficiency by standardizing workflows and reducing variability in care delivery. These systems facilitate faster communication between departments by providing unified access to patient data and actionable insights. Predictive analytics capabilities enable better scheduling, resource allocation, and patient triage, minimizing delays and optimizing utilization of clinical assets. For instance, in April 2026, Abridge partnered with Wolters Kluwer's UpToDate to expand AI clinical decision support (CDS) within its ambient documentation platform. Clinicians access real-time, evidence-based recommendations from patient conversations and notes during visits, including pre-, intra-, and post-encounter.

Global Artificial Intelligence In Clinical Decision Support Market Segmentation

This report forecasts revenue growth at the global, regional, and country levels and provides analysis of the latest trends in each of the sub-segments from 2021 to 2033. For this report, Grand View Research has segmented the global artificial intelligence (AI) in clinical decision support market report based on component, application, deployment mode, end use, and region:

• Component Outlook (Revenue, USD Million, 2021 - 2033)
• Software
• Services
• Application Outlook (Revenue, USD Million, 2021 - 2033)
• Diagnostic Decision Support
• Therapeutic Decision Support
• Medication Management
• Clinical Workflow Optimization
• Predictive Analytics & Risk Assessment
• Others
• Deployment Mode Outlook (Revenue, USD Million, 2021 - 2033)
• Web & Cloud-based
• On-premise
• End use Outlook (Revenue, USD Million, 2021 - 2033)
• Hospitals
• Physician Practices & Ambulatory Clinics
• Pharmaceutical & Biotech Companies
• Payer
• Others
• Regional Outlook (Revenue, USD Million, 2021 - 2033)
• North America
  • U.S.
  • Canada
  • Mexico
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Denmark
  • Sweden
  • Norway
• Asia Pacific
  • Japan
  • China
  • India
  • Australia
  • South Korea
  • Thailand
• Latin America
  • Brazil
  • Argentina
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Kuwait

Table of Contents

Chapter 1. Methodology and Scope

• 1.1. Market Segmentation & Scope
• 1.2. Market Definitions
  • 1.2.1. Component Segment
  • 1.2.2. Application Segment
  • 1.2.3. Deployment Mode Segment
  • 1.2.4. End Use Segment
• 1.3. Information analysis
  • 1.3.1. Market formulation & data visualization
• 1.4. Data validation & publishing
• 1.5. Information Procurement
  • 1.5.1. Primary Research
• 1.6. Information or Data Analysis
• 1.7. Market Formulation & Validation
• 1.8. Market Model
• 1.9. Total Market: CAGR Calculation
• 1.10. Objectives
  • 1.10.1. Objective 1
  • 1.10.2. Objective 2

Chapter 2. Executive Summary

• 2.1. Market Outlook
• 2.2. Segment Snapshot
• 2.3. Competitive Insights Landscape

Chapter 3. Artificial Intelligence (AI) in Clinical Decision Support Market Variables, Trends & Scope

• 3.1. Market Lineage Outlook
  • 3.1.1. Parent market outlook
  • 3.1.2. Related/ancillary market outlook.
• 3.2. Market Dynamics
  • 3.2.1. Market driver analysis
  • 3.2.2. Market restraint analysis
  • 3.2.3. Market opportunity analysis
  • 3.2.4. Market challenges analysis
• 3.3. AI-Driven Therapeutic Decision Support Systems (CDS) Market Analysis Tools
  • 3.3.1. Industry Analysis - Porter's Five Forces Analysis
    • 3.3.1.1. Supplier power
    • 3.3.1.2. Buyer power
    • 3.3.1.3. Substitution threat
    • 3.3.1.4. Threat of new entrant
    • 3.3.1.5. Competitive rivalry
  • 3.3.2. PESTEL Analysis
    • 3.3.2.1. Political landscape
    • 3.3.2.2. Technological landscape
    • 3.3.2.3. Economic landscape
    • 3.3.2.4. Environmental Landscape
    • 3.3.2.5. Legal Landscape
    • 3.3.2.6. Social Landscape

Chapter 4. Artificial Intelligence (AI) in Clinical Decision Support Market: Component Estimates & Trend Analysis

• 4.1. Segment Dashboard
• 4.2. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market Component Movement Analysis
• 4.3. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market Size & Trend Analysis, by Component, 2021 to 2033 (USD Million)
• 4.4. Software
  • 4.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 4.5. Services
  • 4.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)

Chapter 5. Artificial Intelligence (AI) in Clinical Decision Support Market: Application Estimates & Trend Analysis

• 5.1. Segment Dashboard
• 5.2. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market Application Movement Analysis
• 5.3. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market Size & Trend Analysis, by Application, 2021 to 2033 (USD Million)
• 5.4. Diagnostic Decision Support
  • 5.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 5.5. Therapeutic Decision Support
  • 5.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 5.6. Medication Management
  • 5.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 5.7. Predictive Analytics & Risk Assessment
  • 5.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 5.8. Clinical Workflow Optimization
  • 5.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 5.9. Others
  • 5.9.1. Market estimates and forecasts, 2021 to 2033 (USD Million)

Chapter 6. Artificial Intelligence (AI) in Clinical Decision Support Market: Deployment Mode Estimates & Trend Analysis

• 6.1. Segment Dashboard
• 6.2. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market: Deployment Mode Movement Analysis
• 6.3. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market Size & Trend Analysis, by Medical Condition, 2021 to 2033 (USD Million)
• 6.4. Web & Cloud-based
  • 6.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 6.5. On-Premise
  • 6.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)

Chapter 7. Artificial Intelligence (AI) in Clinical Decision Support Market: End Use Estimates & Trend Analysis

• 7.1. Segment Dashboard
• 7.2. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market: End Use Movement Analysis
• 7.3. Global AI-Driven Therapeutic Decision Support Systems (CDS) Market Size & Trend Analysis, by End Use, 2021 to 2033 (USD Million)
• 7.4. Hospitals
  • 7.4.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 7.5. Physician Practices & Ambulatory Clinics
  • 7.5.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 7.6. Pharmaceutical & Biotechnology Companies
  • 7.6.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 7.7. Payers
  • 7.7.1. Market estimates and forecasts, 2021 to 2033 (USD Million)
• 7.8. Others
  • 7.8.1. Market estimates and forecasts, 2021 to 2033 (USD Million)

Chapter 8. Artificial Intelligence (AI) in Clinical Decision Support Market: Regional Estimates & Trend Analysis

• 8.1. Regional Market Share Analysis, 2025 & 2033
• 8.2. Regional Market Dashboard
• 8.3. Market Size & Forecasts Trend Analysis, 2021 to 2033
• 8.4. North America
  • 8.4.1. U.S.
    • 8.4.1.1. Key country dynamics
    • 8.4.1.2. Regulatory framework
    • 8.4.1.3. Competitive scenario
    • 8.4.1.4. U.S. market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.4.2. Canada
    • 8.4.2.1. Key country dynamics
    • 8.4.2.2. Regulatory framework
    • 8.4.2.3. Competitive scenario
    • 8.4.2.4. Canada market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.4.3. Mexico
    • 8.4.3.1. Key country dynamics
    • 8.4.3.2. Regulatory framework
    • 8.4.3.3. Competitive scenario
    • 8.4.3.4. Mexico market estimates and forecasts, 2021 to 2033 (USD Million)
• 8.5. Europe
  • 8.5.1. UK
    • 8.5.1.1. Key country dynamics
    • 8.5.1.2. Regulatory framework
    • 8.5.1.3. Competitive scenario
    • 8.5.1.4. UK market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.2. Germany
    • 8.5.2.1. Key country dynamics
    • 8.5.2.2. Regulatory framework
    • 8.5.2.3. Competitive scenario
    • 8.5.2.4. Germany market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.3. France
    • 8.5.3.1. Key country dynamics
    • 8.5.3.2. Regulatory framework
    • 8.5.3.3. Competitive scenario
    • 8.5.3.4. France market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.4. Italy
    • 8.5.4.1. Key country dynamics
    • 8.5.4.2. Regulatory framework
    • 8.5.4.3. Competitive scenario
    • 8.5.4.4. Italy market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.5. Spain
    • 8.5.5.1. Key country dynamics
    • 8.5.5.2. Regulatory framework
    • 8.5.5.3. Competitive scenario
    • 8.5.5.4. Spain market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.6. Norway
    • 8.5.6.1. Key country dynamics
    • 8.5.6.2. Regulatory framework
    • 8.5.6.3. Competitive scenario
    • 8.5.6.4. Norway market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.7. Sweden
    • 8.5.7.1. Key country dynamics
    • 8.5.7.2. Regulatory framework
    • 8.5.7.3. Competitive scenario
    • 8.5.7.4. Sweden market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.5.8. Denmark
    • 8.5.8.1. Key country dynamics
    • 8.5.8.2. Regulatory framework
    • 8.5.8.3. Competitive scenario
    • 8.5.8.4. Denmark market estimates and forecasts, 2021 to 2033 (USD Million)
• 8.6. Asia Pacific
  • 8.6.1. Japan
    • 8.6.1.1. Key country dynamics
    • 8.6.1.2. Regulatory framework
    • 8.6.1.3. Competitive scenario
    • 8.6.1.4. Japan market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.6.2. China
    • 8.6.2.1. Key country dynamics
    • 8.6.2.2. Regulatory framework
    • 8.6.2.3. Competitive scenario
    • 8.6.2.4. China market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.6.3. India
    • 8.6.3.1. Key country dynamics
    • 8.6.3.2. Regulatory framework
    • 8.6.3.3. Competitive scenario
    • 8.6.3.4. India market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.6.4. Australia
    • 8.6.4.1. Key country dynamics
    • 8.6.4.2. Regulatory framework
    • 8.6.4.3. Competitive scenario
    • 8.6.4.4. Australia market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.6.5. South Korea
    • 8.6.5.1. Key country dynamics
    • 8.6.5.2. Regulatory framework
    • 8.6.5.3. Competitive scenario
    • 8.6.5.4. South Korea market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.6.6. Thailand
    • 8.6.6.1. Key country dynamics
    • 8.6.6.2. Regulatory framework
    • 8.6.6.3. Competitive scenario
    • 8.6.6.4. Thailand market estimates and forecasts, 2021 to 2033 (USD Million)
• 8.7. Latin America
  • 8.7.1. Brazil
    • 8.7.1.1. Key country dynamics
    • 8.7.1.2. Regulatory framework
    • 8.7.1.3. Competitive scenario
    • 8.7.1.4. Brazil market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.7.2. Argentina
    • 8.7.2.1. Key country dynamics
    • 8.7.2.2. Regulatory framework
    • 8.7.2.3. Competitive scenario
    • 8.7.2.4. Argentina market estimates and forecasts, 2021 to 2033 (USD Million)
• 8.8. MEA
  • 8.8.1. South Africa
    • 8.8.1.1. Key country dynamics
    • 8.8.1.2. Regulatory framework
    • 8.8.1.3. Competitive scenario
    • 8.8.1.4. South Africa market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.8.2. Saudi Arabia
    • 8.8.2.1. Key country dynamics
    • 8.8.2.2. Regulatory framework
    • 8.8.2.3. Competitive scenario
    • 8.8.2.4. Saudi Arabia market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.8.3. UAE
    • 8.8.3.1. Key country dynamics
    • 8.8.3.2. Regulatory framework
    • 8.8.3.3. Competitive scenario
    • 8.8.3.4. UAE market estimates and forecasts, 2021 to 2033 (USD Million)
  • 8.8.4. Kuwait
    • 8.8.4.1. Key country dynamics
    • 8.8.4.2. Regulatory framework
    • 8.8.4.3. Competitive scenario
    • 8.8.4.4. Kuwait market estimates and forecasts, 2021 to 2033 (USD Million)

Chapter 9. Competitive Landscape

• 9.1. Company/Competition Categorization
• 9.2. Strategy Mapping
• 9.3. Company Market Position Analysis, 2025
• 9.4. Company Profiles/Listing
  • 9.4.1. UpToDate, Inc. (Wolters Kluwer)
    • 9.4.1.1. Company overview
    • 9.4.1.2. Financial performance
    • 9.4.1.3. Product benchmarking
    • 9.4.1.4. Strategic initiatives
  • 9.4.2. Siemens Healthcare Private Limited
    • 9.4.2.1. Company overview
    • 9.4.2.2. Financial performance
    • 9.4.2.3. Product benchmarking
    • 9.4.2.4. Strategic initiatives
  • 9.4.3. GE Healthcare
    • 9.4.3.1. Company overview
    • 9.4.3.2. Financial performance
    • 9.4.3.3. Product benchmarking
    • 9.4.3.4. Strategic initiatives
  • 9.4.4. Royal Philips NV
    • 9.4.4.1. Company overview
    • 9.4.4.2. Financial performance
    • 9.4.4.3. Product benchmarking
    • 9.4.4.4. Strategic initiatives
  • 9.4.5. Premier Inc.
    • 9.4.5.1. Company overview
    • 9.4.5.2. Financial performance
    • 9.4.5.3. Product benchmarking
    • 9.4.5.4. Strategic initiatives
  • 9.4.6. Natus
    • 9.4.6.1. Company overview
    • 9.4.6.2. Financial performance
    • 9.4.6.3. Product benchmarking
    • 9.4.6.4. Strategic initiatives
  • 9.4.7. PERSYST DEVELOPMENT LLC
    • 9.4.7.1. Company overview
    • 9.4.7.2. Financial performance
    • 9.4.7.3. Product benchmarking
    • 9.4.7.4. Strategic initiatives
  • 9.4.8. Oracle
    • 9.4.8.1. Company overview
    • 9.4.8.2. Financial performance
    • 9.4.8.3. Product benchmarking
    • 9.4.8.4. Strategic initiatives
  • 9.4.9. Aidoc
    • 9.4.9.1. Company overview
    • 9.4.9.2. Financial performance
    • 9.4.9.3. Product benchmarking
    • 9.4.9.4. Strategic initiatives
  • 9.4.10. Viz.ai
    • 9.4.10.1. Company overview
    • 9.4.10.2. Financial performance
    • 9.4.10.3. Product benchmarking
    • 9.4.10.4. Strategic initiatives
  • 9.4.11. Merative
    • 9.4.11.1. Company overview
    • 9.4.11.2. Financial performance
    • 9.4.11.3. Product benchmarking
    • 9.4.11.4. Strategic initiatives
  • 9.4.12. Etiometry
    • 9.4.12.1. Company overview
    • 9.4.12.2. Financial performance
    • 9.4.12.3. Product benchmarking
    • 9.4.12.4. Strategic initiatives