스마트 체외진단 시장 : IVD의 인공지능 - 용도별, 기술별, 제품별, 사용자별 - 경영진 및 컨설턴트용 가이드 포함(2026-2030년)

Report Overview:

Artificial Intelligence will drive IVD market growth over the next 20 years. The market is exploding as physicians use all the information they can get to battle disease. While Pharmaceutical Companies see the potential to make nearly any therapy viable. Find out how this new approach to diagnostics will change medical care forever.

Artificial intelligence (AI) is rapidly transforming the In Vitro Diagnostics (IVD) industry by improving diagnostic accuracy, accelerating data interpretation, and enabling more personalized healthcare decision-making. AI technologies, including machine learning, deep learning, and advanced data analytics, are increasingly integrated into diagnostic workflows across clinical chemistry, molecular diagnostics, pathology, genomics, immunoassays, microbiology, and point-of-care testing. The convergence of AI with advanced diagnostic technologies is creating new opportunities for improved disease detection, workflow efficiency, and healthcare cost optimization.

The global market for AI-enabled in vitro diagnostics is expanding quickly as healthcare providers, diagnostic companies, and life sciences organizations seek to leverage data-driven insights to enhance diagnostic performance. Growth is driven by increasing complexity of diagnostic data, expansion of precision medicine, adoption of digital pathology, and increasing use of genomic and molecular testing technologies. Artificial intelligence is enabling more effective utilization of diagnostic information, improving the clinical value of laboratory testing, and supporting earlier detection of disease.

This market research report provides comprehensive analysis of how AI technologies are reshaping the IVD landscape across multiple diagnostic segments. The report examines technology trends, market drivers, competitive dynamics, regulatory considerations, and strategic opportunities for companies developing AI-enabled diagnostic solutions.

Role of Artificial Intelligence in Diagnostic Innovation

Diagnostic technologies generate large and complex datasets that require sophisticated interpretation to produce clinically meaningful insights. Artificial intelligence enables automated analysis of high-dimensional diagnostic data, allowing identification of patterns that may not be easily detectable using conventional analytical methods.

AI algorithms are increasingly used to support interpretation of medical images, genomic sequences, biomarker panels, and multiplex diagnostic results. Machine learning models can integrate diverse data types, including laboratory results, clinical information, imaging data, and genomic profiles, to improve disease classification and risk assessment.

AI-enabled diagnostics may support earlier detection of disease, improved patient stratification, and more targeted treatment selection. These capabilities are particularly important in oncology, infectious disease management, cardiovascular disease risk assessment, and rare disease diagnosis.

Healthcare systems are increasingly adopting AI tools to improve diagnostic efficiency and reduce variability in clinical interpretation.

AI technologies also support continuous learning from large datasets, enabling ongoing improvement in diagnostic performance.

Key Application Areas

Artificial intelligence is being applied across multiple IVD market segments.

• Digital pathology represents one of the most advanced areas of AI adoption. Machine learning algorithms can analyze histopathology images to identify cancer biomarkers and classify tissue morphology.
• Molecular diagnostics and genomics applications use AI to interpret complex genetic datasets and identify clinically relevant variants.
• Microbiology laboratories use AI tools to identify pathogens and detect antimicrobial resistance patterns.
• Clinical chemistry applications use predictive analytics to identify trends in patient laboratory data.
• Immunoassay testing benefits from AI-assisted interpretation of multiplex biomarker panels.
• Flow cytometry and cell analysis technologies use AI to classify cellular populations and identify rare cell phenotypes.
• Point-of-care diagnostics benefit from AI-enabled interpretation tools that support decentralized healthcare delivery.
• AI technologies may also support laboratory workflow optimization and quality control processes.

Market Drivers

Several factors are driving growth in the AI-enabled IVD market.

• Increasing volume and complexity of diagnostic data is creating demand for advanced analytical tools.
• Expansion of precision medicine initiatives requires integration of genomic and biomarker data.
• Shortage of specialized clinical expertise in some diagnostic disciplines is increasing interest in automated decision support tools.
• Healthcare providers are seeking technologies that improve efficiency and reduce diagnostic variability.
• Advances in cloud computing and data storage technologies enable management of large diagnostic datasets.
• Growth in digital health technologies supports integration of AI-enabled diagnostic tools.
• Regulatory agencies are increasingly developing frameworks supporting use of AI in healthcare applications.
• Investment in healthcare data analytics is supporting innovation in diagnostic technologies.
• Increasing adoption of electronic health records enables integration of AI tools into clinical workflows.

Market Segmentation

The Artificial Intelligence in IVD market can be segmented by diagnostic application, technology type, end user, and geographic region.

• By diagnostic application, digital pathology, molecular diagnostics, clinical chemistry, microbiology, immunoassay testing, and flow cytometry represent key segments.
• By technology type, machine learning, deep learning, natural language processing, and computer vision technologies represent major categories.
• End users include clinical laboratories, hospitals, research institutions, pharmaceutical companies, and diagnostic developers.
• North America represents a major market due to strong digital health infrastructure and investment in healthcare technology innovation.
• Europe represents a significant market supported by regulatory initiatives encouraging adoption of digital health solutions.
• Asia-Pacific markets are expanding due to increasing investment in healthcare technology and growing diagnostic testing volumes.
• Emerging markets represent potential opportunities for AI-enabled diagnostic solutions that improve access to healthcare expertise.

The report includes detailed breakouts for 18 Countries and 4 Regions. A detailed breakout for any country in the world is available to purchasers of the report.

Competitive Landscape

The AI-enabled IVD market includes diagnostic companies, software developers, data analytics firms, and digital health companies.

Competition is influenced by algorithm performance, data quality, regulatory approval status, and integration capabilities with laboratory information systems.

Strategic partnerships between diagnostic companies and artificial intelligence developers are common.

Companies are investing in development of integrated diagnostic platforms combining laboratory instrumentation with AI-enabled data interpretation software.

Data access and training datasets represent important competitive advantages.

Integration with healthcare IT systems influences adoption by clinical laboratories.

Intellectual property related to machine learning algorithms may influence market positioning.

Companies are investing in regulatory compliance strategies to support commercialization of AI-enabled diagnostic tools.

Future Outlook

Artificial intelligence is expected to play an increasingly important role in shaping the future of in vitro diagnostics.

• Advances in machine learning algorithms may improve diagnostic accuracy and enable earlier disease detection.
• Integration of multi-omics datasets may support development of more personalized diagnostic approaches.
• Automation of laboratory workflows may improve operational efficiency.
• AI-enabled decision support tools may improve clinical interpretation of complex diagnostic results.
• Expansion of digital pathology and genomic testing is expected to increase demand for AI-based analytics.
• Increasing collaboration between diagnostic companies and digital health developers may accelerate innovation.

Overall, artificial intelligence represents a transformative force within the in vitro diagnostics industry. Continued advances in data analytics technologies and healthcare digitalization are expected to support sustained market growth and create new opportunities for diagnostic innovation.

Table of Contents

1 Market Guides

• 1.1 Strategic Situation Analysis
• 1.2 Guide for Executives, Marketing, and Business Development Staff
• 1.3 Guide for Management Consultants and Investment Advisors

2 Introduction and Market Definition

• 2.1 What are Smart Diagnostics?
• 2.2 Market Definition
  • 2.2.1 Revenue Market Size
• 2.3 Methodology
  • 2.3.1 Methodology
  • 2.3.2 Sources
  • 2.3.3 Authors
• 2.4 Perspective: Healthcare and the IVD Industry
  • 2.4.1 Global Healthcare Spending
  • 2.4.2 Spending on Diagnostics
  • 2.4.3 Important Role of Insurance for Diagnostics

3 Market Overview

• 3.1 Players in a Dynamic Market
  • 3.1.1 Academic Research Lab
  • 3.1.2 Diagnostic Test Developer
  • 3.1.3 Instrumentation Supplier
  • 3.1.4 Chemical/Reagent Supplier
  • 3.1.5 Pathology Supplier
  • 3.1.6 Independent Clinical Laboratory
  • 3.1.7 Public National/regional Laboratory
  • 3.1.8 Hospital Laboratory
  • 3.1.9 Physicians Office Lab (POLS)
  • 3.1.10 Audit Body
  • 3.1.11 Certification Body
• 3.2 Understanding Artificial Intelligence
  • 3.2.1 Artificial intelligence
  • 3.2.2 Machine learning
  • 3.2.3 Deep learning
  • 3.2.4 Convolutional neural networks
  • 3.2.5 Generative adversarial networks
  • 3.2.6 Limitations
• 3.3 AI Applications in IVD
  • 3.3.1 Infectious Disease
    • 3.3.1.1 Known vs. Unknown
    • 3.3.1.2 TMI
    • 3.3.1.3 Disease surveillance
    • 3.3.1.4 Outbreak detection
    • 3.3.1.5 Contact tracing
    • 3.3.1.6 Forecasting
    • 3.3.1.7 Drug discovery
    • 3.3.1.8 Resource allocation
  • 3.3.2 Oncology
    • 3.3.2.1 Electronic health records
    • 3.3.2.2 Genomic analysis
    • 3.3.2.3 Treatment planning
    • 3.3.2.4 Clinical trial matching
  • 3.3.3 Anatomic Pathology
    • 3.3.3.1 Image analysis
    • 3.3.3.2 Tumor segmentation
    • 3.3.3.3 Disease classification
    • 3.3.3.4 Predictive modeling
    • 3.3.3.5 Quality control
    • 3.3.3.6 Digital pathology
  • 3.3.4 Cardiology
    • 3.3.4.1 Electrocardiogram analysis
    • 3.3.4.2 Electronic health records
    • 3.3.4.3 Genomic analysis
    • 3.3.4.4 Treatment planning
    • 3.3.4.5 Prediction of outcomes
  • 3.3.5 Diabetes
    • 3.3.5.1 Diagnosis
    • 3.3.5.2 Blood glucose monitoring
    • 3.3.5.3 Personalized treatment plans
    • 3.3.5.4 Medication management
    • 3.3.5.5 Diabetes education
    • 3.3.5.6 Predictive analytics
  • 3.3.6 General Medicine
    • 3.3.6.1 Diagnosis
    • 3.3.6.2 Predictive Analytics
    • 3.3.6.3 Personalized Treatment Plans
    • 3.3.6.4 Medication Management
    • 3.3.6.5 Disease Monitoring
    • 3.3.6.6 Telemedicine

4 Market Trends

• 4.1 Factors Driving Growth
  • 4.1.1 Outcome Improvement
  • 4.1.2 The Aging Effect
  • 4.1.3 Cost Containment
  • 4.1.4 Physician Impact
  • 4.1.5 Cost of Intelligence
• 4.2 Factors Limiting Growth
  • 4.2.1 State of knowledge
  • 4.2.2 Genetic Blizzard
  • 4.2.3 Protocol Resistance
  • 4.2.4 Regulation and coverage
• 4.3 Instrumentation, Automation and Diagnostic Trends
  • 4.3.1 Traditional Automation and Centralization
  • 4.3.2 The New Automation, Decentralization and Point Of Care
  • 4.3.3 Instruments Key to Market Share
  • 4.3.4 Bioinformatics Plays a Role
  • 4.3.5 PCR Takes Command
  • 4.3.6 Next Generation Sequencing Fuels a Revolution
  • 4.3.7 NGS Impact on Pricing
  • 4.3.8 Whole Genome Sequencing, A Brave New World
  • 4.3.9 Companion Diagnostics Blurs Diagnosis and Treatment
  • 4.3.10 Shifting Role of Diagnostics

5 Recent Developments

• 5.1 Recent Developments – Importance and How to Use This Section
  • 5.1.1 Importance of These Developments
  • 5.1.2 How to Use This Section
• 5.2 Ataraxis AI Nabs Financing
• 5.3 Myriad Genetics Licenses Image Analysis Technology
• 5.4 Danaher, AI Firm I Form Investment Partnership
• 5.5 Cardio Dx AI-Based Tests Receive Final CMS Pricing
• 5.6 Ataraxis AI Launches AI Cancer Dx
• 5.7 Tempus Immuno-Oncology Portfolio AI-enabled
• 5.8 AI enables precision diagnosis of cervical cancer
• 5.9 UK to Rollout Digital Pathology Across NHS
• 5.10 AI Based Next-Generation Colorectal Cancer Test
• 5.11 Evident, Corista, Sakura Finetek, Visiopharm Form Digital Pathology Alliance
• 5.12 Viome Life Sciences Raises $86.5M in Oversubscribed Series C Round
• 5.13 Becton Dickinson Gets Clearance for AI-Based Bacterial Imaging
• 5.14 Paige, Leica Biosystems Expand Digital Pathology Partnership
• 5.15 Clarapath Acquires Digital Pathology Company Crosscope
• 5.16 CanSense to Develop Colorectal Cancer Test
• 5.17 Owkin-led Machine Learning Study IDs Cancer Treatment Biomarkers
• 5.18 Guardant Health to Integrate Lunit's AI PD-L1 Algorithm
• 5.19 Vesale Bioscience to Develop AI Phage Therapy Diagnostic Platform
• 5.20 Caris Life Sciences To Use AI and Machine Learning
• 5.21 Numares Health To Develop AI for “Metabolite Constellations”
• 5.22 Sepsis Testing Startup DeepUll to Use AI for Medical Decisions
• 5.23 Viome Life Sciences Raises $67M in Series C Financing For AI Cancer Dx
• 5.24 ADM Diagnostics Wins Grant for Brain Injury Test Development
• 5.25 Paige to Develop New AI-based Pathology Test
• 5.26 Aiforia Gains CE-IVD Mark for AI-Powered Histopathology
• 5.27 Genetic Profiling May Identify Patients Who Do Not Need Radiation Therapy
• 5.28 Thermo Fisher Introduces Homologous Score for Cancer Profiling
• 5.29 Genomic Test IDs Cancer Cells Early

6 Profiles of Key Players

• 6.1 Adaptive Biotechnologies
• 6.2 Aidoc
• 6.3 Anumana
• 6.4 ARUP Laboratories
• 6.5 Atomwise
• 6.6 Bayesian Health
• 6.7 Behold.ai
• 6.8 BGI Genomics Co. Ltd
• 6.9 bioMerieux Diagnostics
• 6.10 Bio-Rad Laboratories, Inc
• 6.11 Cambridge Cognition
• 6.12 Cardiologs (Phillips)
• 6.13 CareDx
• 6.14 Caris Molecular Diagnostics
• 6.15 Cleerly
• 6.16 ClosedLoop AI
• 6.17 CloudMedX Health
• 6.18 Deepcell
• 6.19 Digital Diagnostics
• 6.20 EKF Diagnostics Holdings
• 6.21 Freenome
• 6.22 GE Healthcare
• 6.23 Glooko
• 6.24 Idoven
• 6.25 Illumina
• 6.26 Infohealth
• 6.27 Jade
• 6.28 K Health
• 6.29 Lunit
• 6.30 Luventix
• 6.31 MaxCyte
• 6.32 Mayo Clinic Laboratories
• 6.33 Medtronic
• 6.34 Merative
• 6.35 Nanox
• 6.36 NIOX Group
• 6.37 Niramai Health Analytix
• 6.38 NVIDIA
• 6.39 Oncohost
• 6.40 OraLiva
• 6.41 Owkin
• 6.42 Oxford Nanopore Technologies
• 6.43 Pacific Biosciences
• 6.44 Paige.AI
• 6.45 PathAI
• 6.46 Perthera
• 6.47 Philips Healthcare
• 6.48 Prognos
• 6.49 Qiagen
• 6.50 Qure.ai
• 6.51 Renalytix
• 6.52 Seegene
• 6.53 Siemens Healthineers
• 6.54 Sophia Genetics
• 6.55 Sysmex
• 6.56 Viz.ai

7 The Global Market for Smart Diagnostics

• 7.1 Global Market Overview by Country
  • 7.1.1 Table – Global Market by Country
  • 7.1.2 Chart - Global Market by Country
• 7.2 Global Market by Application - Overview
  • 7.2.1 Table – Global Market by Application
  • 7.2.2 Chart – Global Market by Application – Base/Final Year Comparison
  • 7.2.3 Chart – Global Market by Application – Base Year
  • 7.2.4 Chart – Global Market by Application – Final Year
  • 7.2.5 Chart – Global Market by Application – Share by Year
  • 7.2.6 Chart – Global Market by Application – Segment Growth
• 7.3 Global Market by Technology - Overview
  • 7.3.1 Table – Global Market by Technology
  • 7.3.2 Chart – Global Market by Technology – Base/Final Year Comparison
  • 7.3.3 Chart – Global Market by Technology – Base Year
  • 7.3.4 Chart – Global Market by Technology – Final Year
  • 7.3.5 Chart – Global Market by Technology – Share by Year
  • 7.3.6 Chart – Global Market by Technology – Segment Growth
• 7.4 Global Market by Place - Overview
  • 7.4.1 Table – Global Market by Place
  • 7.4.2 Chart – Global Market by Place – Base/Final Year Comparison
  • 7.4.3 Chart – Global Market by Place – Base Year
  • 7.4.4 Chart – Global Market by Place – Final Year
  • 7.4.5 Chart – Global Market by Place – Share by Year
  • 7.4.6 Chart – Global Market by Place – Segment Growth
• 7.5 Global Market by Product - Overview
  • 7.5.1 Table – Global Market by Product
  • 7.5.2 Chart – Global Market by Product – Base/Final Year Comparison
  • 7.5.3 Chart – Global Market by Product – Base Year
  • 7.5.4 Chart – Global Market by Product – Final Year
  • 7.5.5 Chart – Global Market by Product – Share by Year
  • 7.5.6 Chart – Global Market by Product – Segment Growth

8 Global Markets – By Application

• 8.1 Cancer
  • 8.1.1 Table Cancer Testing – by Country
  • 8.1.2 Chart - Cancer Testing Growth
• 8.2 Infectious Disease Testing
  • 8.2.1 Table Infectious Disease Testing – by Country
  • 8.2.2 Chart – Infectious Disease Testing Growth
• 8.3 Metabolic Testing
  • 8.3.1 Table Metabolic Testing – by Country
  • 8.3.2 Chart - Metabolic Testing Growth
• 8.4 Cardiac Testing
  • 8.4.1 Table Cardiac Testing – by Country
  • 8.4.2 Chart - Cardiac Testing Growth
• 8.5 Diabetes Testing
  • 8.5.1 Table Diabetes Testing – by Country
  • 8.5.2 Chart - Diabetes Testing Growth
• 8.6 Other Disease Testing
  • 8.6.1 Table Other Disease Testing – by Country
  • 8.6.2 Chart – Other Disease Testing Growth

9 Global Markets – By Technology

• 9.1 NGS Technology
  • 9.1.1 Table NGS Technology – by Country
  • 9.1.2 Chart – NGS Technology Growth
• 9.2 PCR Technology
  • 9.2.1 Table PCR Technology – by Country
  • 9.2.2 Chart – PCR Technology Growth
• 9.3 Chemistry/IA Technology
  • 9.3.1 Table Chemistry/IA Technology – by Country
  • 9.3.2 Chart - Chemistry/IA Technology Growth
• 9.4 Pathology Technology
  • 9.4.1 Table Pathology Technology – by Country
  • 9.4.2 Chart - Pathology Technology Growth
• 9.5 Other Technology
  • 9.5.1 Table Other Technology – by Country
  • 9.5.2 Chart - Other Technology Growth

10 Global Markets – By Place

• 10.1 Research
  • 10.1.1 Table Research – by Country
  • 10.1.2 Chart – Research Growth
• 10.2 Pharmaceutical Research
  • 10.2.1 Table Pharmaceutical Research – by Country
  • 10.2.2 Chart - Pharmaceutical Research Growth
• 10.3 Clinical
  • 10.3.1 Table Clinical – by Country
  • 10.3.2 Chart - Clinical Growth
• 10.4 Other Place
  • 10.4.1 Table Other Place – by Country
  • 10.4.2 Chart – Other Place Growth

11 Global Markets – By Product

• 11.1 Instruments
  • 11.1.1 Table Instruments – by Country
  • 11.1.2 Chart – Instruments Growth
• 11.2 Assay
  • 11.2.1 Table Assay – by Country
  • 11.2.2 Chart - Assay Growth
• 11.3 Software
  • 11.3.1 Table Software – by Country
  • 11.3.2 Chart - Software Growth
• 11.4 Services
  • 11.4.1 Table Services – by Country
  • 11.4.2 Chart - Services Growth
• 11.5 Other Product
  • 11.5.1 Table Other Product – by Country
  • 11.5.2 Chart – Other Product Growth

12 Appendices

• 12.1 United States Clinical Laboratory Fees Schedule
  • 12.1.1 Laboratory Fees Schedule
  • 12.1.2 The Most Used IVD Assays
  • 12.1.3 The Highest Grossing Assays