AI 활용 진단 방사선유전체학 디바이스 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the AI-Powered diagnostic radiogenomics devices market size is projected to expand from USD 1.15 billion in 2025 and USD 1.37 billion in 2026 to USD 3.36 billion by 2031, registering a CAGR of 19.65% between 2026 to 2031.

This report is Segmented by Component (Hardware, Software, Services), Imaging Modality (MRI, CT. And More), Genomic Input (DNA Variants, RNA Expression and Transcriptomics, and More), Clinical Application (Mutation and Biomarker Status Prediction and More), End User (Hospitals and Clinics and More), and Geography (North America and More). Market Forecasts are Provided in Terms of Value (USD).

Global AI-Powered Diagnostic Radiogenomics Devices Market Trends and Insights

Rising Demand for Multimodal Precision Diagnostics

The AI-powered diagnostic radiogenomics devices market is moving away from single-source analysis because clinicians increasingly want one workflow that can combine imaging, genomic, transcriptomic, proteomic, and patient record data. Multimodal AI systems are showing better performance in molecular subtype prediction and treatment response assessment than models built on one data type alone, which is making them more relevant for precision care pathways. A major shift is that these systems no longer depend on complete cross-modal data from every patient, because newer model designs can infer missing inputs from the data that are already available. That change is widening access for hospitals with partial genomics coverage, and it is bringing more institutions into the AI-powered diagnostic radiogenomics devices market without forcing them to rebuild their entire diagnostic pathway at once. The HONeYBEE framework also shows how modular embeddings across DNA methylation, gene expression, and somatic mutations can support lower-cost deployment at the institution level. As demand comes from oncology, neurology, and cardiovascular teams at the same time, the AI-powered diagnostic radiogenomics devices market is gaining a broader clinical base than a pure oncology tool set would normally deliver.

Expanding Oncology-First Clinical Use Cases

The AI-powered diagnostic radiogenomics devices market is still led by oncology, but the use case is moving past detection toward treatment selection, trial enrollment, and ongoing response assessment. Lunit presented data at AACR 2025 showing AI-based prediction of EGFR mutations in non-small cell lung cancer from stained tissue images, which supports faster early screening before full molecular workup is completed. Drug developers are helping push this adoption because they need biomarker-linked tools that can support companion diagnostic development and reduce uncertainty in patient selection. A registered 2026 ClinicalTrials.gov study in esophageal cancer shows that radiomics, pathomics, genomics, and other multi-omics layers are already being combined to predict treatment response and prognosis beyond the most established tumor settings. A 2025 hepatocellular carcinoma publication also showed that a machine learning radiogenomics biomarker could separate prognosis across major imaging modalities with a hazard ratio range of 1.415 to 1.890. As this indication set extends from lung, brain, and breast into GI, prostate, and hepatic cancers, the AI-powered diagnostic radiogenomics devices market gains revenue potential that is larger than a narrow mutation screening model would suggest.

Limited Clinical-Grade Labelled Radiogenomics Datasets

The AI-powered diagnostic radiogenomics devices market still faces a basic supply problem because high-quality labeled datasets that connect imaging features with confirmed molecular pathology remain scarce. Many current models are built on retrospective cohorts where imaging and genomic data were collected at different times and for different clinical purposes, which introduces noise and weakens transferability across sites. The issue is more severe for transcriptomic and proteomic layers, because those assays are not part of most routine imaging encounters and cannot be added retrospectively without extra cost. Federated learning helps by allowing model development across institutions without moving patient data, but it does not remove the challenge of inconsistent labels and uneven data quality. The European Cancer Imaging Initiative had connected 83 imaging datasets across 9 cancer types, covering 107,000 subjects by September 2025, which shows progress but still falls short of what broad clinical-grade generalization needs. Until prospective multimodal data collection becomes routine rather than research-led, this limitation will continue to cap model performance and slow scaling in the AI-powered diagnostic radiogenomics devices market.

Other drivers and restraints analyzed in the detailed report include:

1. Increasing Hospital-Level AI Workflow Integration
2. Growing Genomic Data Availability for Model Training
3. Reimbursement Uncertainty for AI-Enabled Companion Diagnostics

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software accounted for 51.38% of revenue in 2025, which made it the largest component in the AI-powered diagnostic radiogenomics devices market. That position reflects the central role of inference engines, model management layers, and clinical decision support platforms, because these elements sit at the point where imaging and genomic signals are turned into usable clinical output. The software lead also shows that buyers continue to prioritize flexible deployment over fixed equipment replacement, especially where hospitals already own core imaging assets. Even so, the product layer is becoming less exclusive as more open architectures and foundation model frameworks enter the field, which means simple access to an algorithm is no longer enough to protect margins in the AI-powered diagnostic radiogenomics devices market. The stronger differentiator is whether a vendor can place that software into a live hospital environment and keep it compliant, stable, and useful over time.

Services is projected to advance at a 23.87% CAGR through 2031, which makes it the fastest-growing component in the AI-powered diagnostic radiogenomics devices market. Hospitals are increasingly favoring managed contracts that cover validation, deployment, monitoring, and regulatory updates because those tasks stretch local IT and clinical operations teams. This shift matters because it moves value from the software license alone toward the full operating model that keeps multimodal AI functioning in routine care. Hardware remains important, especially where imaging OEMs embed AI acceleration and neural processing capabilities into MRI, CT, and PET systems, but its growth is slower because replacement cycles are longer and many AI functions can still be added through upgrades. In practical terms, the AI-powered diagnostic radiogenomics devices industry is moving toward service-backed platforms where revenue durability depends on integration depth rather than on one-time installation.

MRI held 38.13% share in 2025, which kept it as the leading modality in the AI-powered diagnostic radiogenomics devices market. Its lead comes from strong soft-tissue contrast and its established use in brain, prostate, and breast cancer workflows, where genomic correlation has clearer clinical relevance. These settings continue to support high-value use cases such as glioma mutation prediction, multiparametric prostate imaging, and breast lesion characterization. GE HealthCare presented Decipher-MR in 2026 as a clinical-grade 3D MRI foundation model trained on 200,000 MRI series, which underlines how MRI remains a major development focus for broad AI imaging applications. As a result, MRI still sets the revenue floor for the AI-powered diagnostic radiogenomics devices market even while other modalities expand around it.

Multi-modal fusion is forecast to grow at a 24.15% CAGR through 2031, which makes it the fastest-moving modality layer in the AI-powered diagnostic radiogenomics devices market. The key change is that clinically useful fusion no longer requires every data source to be acquired in the same session, because retrospective combination across timepoints is becoming more practical. That greatly increases the number of patients and institutions that can be included in AI workflows. Spanish work presented through VHIO showed that simultaneous PET and MRI can produce anatomical and functional tumor information that single modalities cannot replicate in prostate and hepatocellular carcinoma settings. CT, PET, ultrasound, and X-ray or mammography still contribute meaningful revenue, but fusion architectures are raising the clinical ceiling because they support richer radiomic phenotypes and closer links to genomic interpretation. This is where the AI-powered diagnostic radiogenomics devices industry is shifting from a modality-led structure toward a data-combination structure.

Complete Report Scope:

• By Component
  • Hardware
  • Software
  • Services
• By Imaging Modality
  • MRI
  • CT
  • PET
  • Ultrasound
  • X-Ray and Mammography
  • Multi-Modal Fusion
• By Genomic Input
  • DNA Variants
  • RNA Expression and Transcriptomics
  • Epigenetics
  • Proteomics
  • Liquid Biopsy Signals
• By Clinical Application
  • Mutation and Biomarker Status Prediction
  • Tumor Detection
  • Prognosis and Survival Risk Stratification
  • Therapy Response Prediction and Monitoring
  • Recurrence Versus Treatment Effect Discrimination
  • Patient Selection for Trials
• By End User
  • Hospitals and Clinics
  • Diagnostic Imaging Centers
  • Academic and Research Institutes and Biobanks
  • Pharma and Biotech and CROs
  • Government and Public Health Programs
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa
    • GCC
    • South Africa
    • Rest of Middle East and Africa
  • South America
    • Brazil
    • Argentina
    • Rest of South America

Geography Analysis

North America accounted for 44.64% of revenue in 2025, which gave it the largest regional position in the AI-powered diagnostic radiogenomics devices market. The region benefits from a dense concentration of academic cancer centers, established imaging infrastructure, and close collaboration between vendors and reference institutions. GE HealthCare and Mayo Clinic launched the MI-BET theranostics collaboration in July 2026, combining StarGuide SPECT/CT, MIM LesionID Pro, and blood-based biomarkers for advanced prostate cancer, which shows how imaging and biomarker integration are moving into high-priority treatment settings. Hospital networks in the United States also provide a favorable base for service-led deployment, because enterprise contracts can cover validation, reporting, and follow-up model management together. This keeps North America central to both product testing and commercial proof in the AI-powered diagnostic radiogenomics devices market.

Europe remained the second-largest region in the AI-powered diagnostic radiogenomics devices market, supported by coordinated imaging data programs and strong oncology research infrastructure. Germany's approval of low-dose CT lung cancer screening for statutory health insurance created a larger real-world setting for AI-assisted reading and biomarker extraction from April 2026 onward, as referenced in the supplied draft. At the same time, GDPR and EU AI Act requirements are pushing vendors toward privacy-preserving and more tightly governed deployment models, which can slow implementation but strengthen long-term trust.

Asia-Pacific is projected to expand at a 25.72% CAGR through 2031, making it the fastest-growing region in the AI-powered diagnostic radiogenomics devices market. Japan stands out because reimbursement for Illumina's TruSight Oncology Comprehensive system took effect on June 1, 2026, which broadens access to genomic profiling that can feed AI-supported oncology workflows. Japan is also contributing to explainable genomic AI through work at the University of Tokyo on structural anomaly interpretation in cancer genomes. China offers scale because AI imaging is being used to address radiologist shortages and uneven access between urban and rural settings. At the same time, South Korea continues to build export-facing oncology imaging capability through its AI companies. South America, the Middle East, and Africa are still early-stage markets. Still, public health programs, biobank partnerships, and national genomics institutes are gradually creating entry points for the AI-powered diagnostic radiogenomics devices market. Across geography, the AI-powered diagnostic radiogenomics devices market size is becoming more evenly influenced by policy support, data availability, and workflow readiness rather than by imaging infrastructure alone.

1. Agilent Technologies
2. Aidoc Medical Ltd.
3. Canon
4. CureMetrix, Inc.
5. GE HealthCare Technologies Inc.
6. Imbio LLC
7. Koninklijke Philips
8. Lunit
9. Median Technologies SA
10. Mirada Medical Limited
11. Owkin
12. PathAI, Inc.
13. Perspectum Ltd.
14. Qlucore AB
15. QMENTA Inc.
16. Quibim S.L.
17. Radiomics.bio Inc.
18. Seno Medical Instruments, Inc.
19. Siemens Healthineers
20. SOPHiA GENETICS SA
21. Tempus AI, Inc.
22. Thermo Fisher Scientific
23. Ultromics Limited
24. VUNO Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rising Demand for Multimodal Precision Diagnostics
  • 4.2.2 Expanding Oncology-First Clinical Use Cases
  • 4.2.3 Increasing Hospital-Level AI Workflow Integration
  • 4.2.4 Growing Genomic Data Availability for Model Training
  • 4.2.5 Wider Use in Therapy Response and Trial Stratification
  • 4.2.6 Cross-Validation Pressure From Explainability and Auditability
• 4.3 Market Restraints
  • 4.3.1 Limited Clinical-Grade Labelled Radiogenomics Datasets
  • 4.3.2 Interoperability Gaps Across PACS, EHR, and Omics Systems
  • 4.3.3 High Validation Burden for Multi-Site Regulatory Clearance
  • 4.3.4 Reimbursement Uncertainty for AI-Enabled Companion Diagnostics
• 4.4 Value Chain Analysis
• 4.5 Technological Outlook
• 4.6 Regulatory Landscape
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Threat of New Entrants
  • 4.7.2 Bargaining Power of Buyers
  • 4.7.3 Bargaining Power of Suppliers
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE, USD)

• 5.1 By Component
  • 5.1.1 Hardware
  • 5.1.2 Software
  • 5.1.3 Services
• 5.2 By Imaging Modality
  • 5.2.1 MRI
  • 5.2.2 CT
  • 5.2.3 PET
  • 5.2.4 Ultrasound
  • 5.2.5 X-Ray and Mammography
  • 5.2.6 Multi-Modal Fusion
• 5.3 By Genomic Input
  • 5.3.1 DNA Variants
  • 5.3.2 RNA Expression and Transcriptomics
  • 5.3.3 Epigenetics
  • 5.3.4 Proteomics
  • 5.3.5 Liquid Biopsy Signals
• 5.4 By Clinical Application
  • 5.4.1 Mutation and Biomarker Status Prediction
  • 5.4.2 Tumor Detection
  • 5.4.3 Prognosis and Survival Risk Stratification
  • 5.4.4 Therapy Response Prediction and Monitoring
  • 5.4.5 Recurrence Versus Treatment Effect Discrimination
  • 5.4.6 Patient Selection for Trials
• 5.5 By End User
  • 5.5.1 Hospitals and Clinics
  • 5.5.2 Diagnostic Imaging Centers
  • 5.5.3 Academic and Research Institutes and Biobanks
  • 5.5.4 Pharma and Biotech and CROs
  • 5.5.5 Government and Public Health Programs
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 Europe
    • 5.6.2.1 Germany
    • 5.6.2.2 United Kingdom
    • 5.6.2.3 France
    • 5.6.2.4 Italy
    • 5.6.2.5 Spain
    • 5.6.2.6 Rest of Europe
  • 5.6.3 Asia-Pacific
    • 5.6.3.1 China
    • 5.6.3.2 India
    • 5.6.3.3 Japan
    • 5.6.3.4 South Korea
    • 5.6.3.5 Australia
    • 5.6.3.6 Rest of Asia-Pacific
  • 5.6.4 Middle East and Africa
    • 5.6.4.1 GCC
    • 5.6.4.2 South Africa
    • 5.6.4.3 Rest of Middle East and Africa
  • 5.6.5 South America
    • 5.6.5.1 Brazil
    • 5.6.5.2 Argentina
    • 5.6.5.3 Rest of South America

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Market Share Analysis
• 6.3 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.3.1 Agilent Technologies, Inc.
  • 6.3.2 Aidoc Medical Ltd.
  • 6.3.3 Canon Medical Systems Corporation
  • 6.3.4 CureMetrix, Inc.
  • 6.3.5 GE HealthCare Technologies Inc.
  • 6.3.6 Imbio LLC
  • 6.3.7 Koninklijke Philips N.V.
  • 6.3.8 Lunit Inc.
  • 6.3.9 Median Technologies SA
  • 6.3.10 Mirada Medical Limited
  • 6.3.11 Owkin
  • 6.3.12 PathAI, Inc.
  • 6.3.13 Perspectum Ltd.
  • 6.3.14 Qlucore AB
  • 6.3.15 QMENTA Inc.
  • 6.3.16 Quibim S.L.
  • 6.3.17 Radiomics.bio Inc.
  • 6.3.18 Seno Medical Instruments, Inc.
  • 6.3.19 Siemens Healthineers AG
  • 6.3.20 SOPHiA GENETICS SA
  • 6.3.21 Tempus AI, Inc.
  • 6.3.22 Thermo Fisher Scientific Inc.
  • 6.3.23 Ultromics Limited
  • 6.3.24 VUNO Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment