AI Medical Image Reading Assistant System Market to Reach $30.7 Billion by 2031 at 27.1% CAGR

The global AI Medical Image Reading Assistant System market is experiencing explosive growth, projected to expand from US$ 5.73 billion in 2024 to US$ 30.7 billion by 2031, achieving a CAGR of 27.1% during the forecast period from 2025 to 2031, according to the latest QYResearch report.
This AI-powered diagnostic system is transforming radiology workflows by supporting early disease screening, risk assessment, and precision diagnosis. Increasing demand across oncology, neurology, pulmonary, and musculoskeletal domains is accelerating adoption in both developed and emerging healthcare markets.
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Leading Players and Recent Developments
Major companies dominating the AI medical image reading space include:
• Siemens Healthineers
• GE HealthCare
• Philips Healthcare
• Canon Medical Systems
• Fujifilm Healthcare
• Hitachi Healthcare
• Samsung Medison
• Carestream Health
• Agfa-Gevaert Group
• Konica Minolta Healthcare
• Aidoc
• Lunit
• Arterys
• VUNO
• Deepwise Healthcare
• InferVision
• ScreenPoint Medical
• Google Health, IBM Watson Health, Microsoft Cloud for Healthcare
• Tencent Healthcare, Alibaba Health
In 2025, Aidoc launched its upgraded aiOS platform across 22 sites in collaboration with Advocate Health, enabling real-time triage and diagnostic support. Similarly, Lunit expanded its AI chest screening solutions in Southeast Asia, while Deepwise began large-scale deployments in Chinese tertiary hospitals.
Top 5 AI Medical Imaging Products
Aidoc - aiOSTM Platform
• Description: Enterprise grade clinical AI operating system coordinating >17 FDA cleared diagnostic algorithms across radiology, neurovascular, vascular, cardiology workflows.
• Deployment: Integrated into >1,200 health systems globally, used in >900 hospitals and imaging centers.
• Functionality: Real time triage alerts (e.g. intracranial hemorrhage, pulmonary embolism), care coordination, automatic report generation, patient management.
• Recent news: In July 2025 Advocate Health expanded deployment across 22 sites for ~63,000 patients/year benefit.
• Latest development: $150M funding in July 2025 to scale its CARE foundation model on aiOS platform globally.
Lunit - INSIGHT CXR4
• Description: AI solution for chest X ray analysis detecting up to 12 abnormalities with DualScan architecture.
• Performance: AUC 97 99% overall, workload reduction by ~36.2%, correctly flags ~92.3% of normal CXRs while detecting ~95% of urgent findings.
• Clinical studies: AUC 0.93 for lung nodule detection with sensitivity ~89% vs human AUC 0.81 and specificity ~80%.
• Deployment: Integrated into Samsung X ray devices starting Jan 2024, targeting US, Canada, Europe then Southeast Asia.
Deepwise - Dr.Wise® Chest CT Diagnosis System
• Description: Multi-sign, multi-disease AI system for chest CT, analyzing lung diseases, generating lesion tracking and comparisons.
• Key benefits: High sensitivity and specificity; faster than manual reads; supports lesion matching for follow-up.
• Usage: Supports early screening for lung disease and workflow automation.
• Regulatory note: First Chinese AI medical device approval for breast cancer imaging granted by Deepwise; chest CT tool part of that expansion.
ScreenPoint Medical - Transpara® Breast AI
• Description: FDA cleared AI software for 2D/3D mammography with risk scoring and lesion detection.
• Scale: Used to read over 10 million mammograms globally; most peer reviewed publications in breast AI.
• Workflow impact: Improves detection in dense tissue, lowers recall rates, enhances workflow.
• Integration: Integrated with Ikonopedia in June 2025 for seamless breast imaging workflows.
Arterys - Cloud based Radiology AI Platform
• Description: Cloud-native AI platform for CT, MRI, and ultrasound; integrates with PACS/EHR, supports 4D flow and advanced visualizations.
• Innovation: First FDA approved clinical cloud based deep learning imaging system.
• Use cases: Supports emergency diagnostics, research, and precision medicine applications.
• Corporate update: Became part of Tempus in 2025 to scale within broader precision medicine ecosystem.
Regional Highlights
Asia-Pacific and North America lead market expansion, driven by national healthcare digitization policies and AI integration mandates. Europe is gaining momentum amid regulatory clarity under the EU AI Act (effective 2024). Key country-specific projections for 2031 highlight strong growth in the U.S., China, Japan, South Korea, India, Germany, and the UK.
Segmentation by Application
The market is categorized by application as follows:
• Tumor Detection and Screening
• Pulmonary Disease Diagnosis
• Neurological Disease Analysis
• Musculoskeletal System Analysis
• Others
Among these, tumor screening holds the largest share in 2025, with neurological analysis growing rapidly due to AI models capable of detecting early-stage Alzheimer's and stroke-related anomalies.
Segmentation by Modality
Based on imaging modality, the system types include:
• X-ray Image Reading Assistant
• CT Image Reading Assistant
• MRI Image Reading Assistant
• Ultrasound Image Reading Assistant
• Others
CT and MRI assistant systems dominate the high-end diagnostic segment, while AI-powered X-ray solutions remain essential in frontline screening programs, especially in low-resource environments.
Emerging Trends
Explainable AI
In 2025, explainable AI (XAI) has moved from theory into practice. Recent studies show an explainable fully convolutional anomaly detection model for breast MRI datasets achieved a detection AUC of 84% in balanced prevalence and 72% in low prevalence cohorts-outperforming traditional binary classification models (81% and 69% respectively), while spatial agreement improved to 92% AUC compared to 81% for conventional models. Meanwhile, a roadmap for integrating explainable AI into clinical radiology emphasizes the need for genuine AI human dialogue and multimodal interpretability to support complex scenarios like stroke treatment, highlighting both progress and remaining challenges.
Generative AI Reporting
Large corporate collaborations are accelerating adoption of generative AI in radiology workflows. In mid 2024, Bayer and Rad AI announced integration of Rad AI's generative reporting tools into Bayer's Calantic platform. The tools include AI powered speech recognition, "Omni Impressions" for dynamic report drafting, and "Continuity" modules for automating follow ups on incidental findings. As of June 2025, Rad AI has been named to the CNBC Disruptor 50 list and is now deployed in more than half of U.S. health systems, including 9 of the 10 largest radiology practices. Generative AI tools now alleviate administrative burden, reducing radiologist fatigue and enabling faster report turnaround times while preserving diagnostic precision.
Real world Deployment at Scale
In early 2025, South Australia Medical Imaging (SAMI) implemented Annalise.ai's Enterprise CXR solution across six metropolitan and four regional hospitals statewide, covering approximately 700,000 examinations per year (~21% of their imaging volume) and benefiting a staff of over 1,200 professionals. The system detects up to 124 chest X ray findings in under 20 seconds per study, providing a rapid "second read" that improves reporting accuracy and speeds up triaging-marking the first public radiology system-wide AI deployment in Australia.
Regulatory Acceleration & Iterative Upgrades
Late 2024 saw the U.S. FDA begin implementing its Predetermined Change Control Plan (PCCP) framework, allowing approved AI tools to undergo controlled updates without full re submission. This enables more frequent algorithm refinements and quicker clinical integration. The regulatory shift reflects a broader industry consensus: AI in radiology must evolve continuously while maintaining safety, performance, and traceability. As of mid 2025, over 1,000 AI/ML-enabled medical imaging tools have received regulatory clearance-mostly using traditional machine learning, but with growing frameworks supporting generative and explainable systems.
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Aidoc aiOSTM - Verified Customer Institutions
• Mount Sinai Health System
• Yale New Haven Health System
• Northwell Health
• University of Miami Health System
• Temple Health
• Cedars Sinai Medical Center
• University of Rochester Medical Center
• Hartford HealthCare
• Sutter Health
• WellSpan Health
• Hadassah Medical Center
• Sourasky Medical Center
• Rabin Medical Center
• Assuta Medical Centers
• Shaare Zedek Medical Center

Driven by rapid algorithmic advances, growing clinical demand, and supportive regulatory frameworks, the global AI Medical Image Reading Assistant System market is poised for long-term transformation. Leading vendors are scaling deployment across radiology departments, while healthcare systems increasingly recognize AI as essential infrastructure for early detection and precision medicine.

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