Table of Contents
Overview
New York, NY – Dec 23, 2025 – The Global AI In Endoscopy Market size is expected to be worth around USD 838.9 Million by 2033 from USD 75.9 Million in 2024, growing at a CAGR of 30.6% during the forecast period from 2024 to 2033.
The integration of artificial intelligence (AI) in endoscopy is increasingly being recognized as a transformative development in gastrointestinal diagnostics and interventional procedures. AI-powered endoscopy solutions are designed to support clinicians by enhancing lesion detection, characterization, and real-time decision-making during endoscopic examinations.
AI algorithms, primarily based on deep learning and computer vision, are trained on large volumes of annotated endoscopic images and videos. These systems can identify subtle mucosal abnormalities, polyps, and early-stage neoplastic lesions that may be difficult to detect through conventional visual assessment alone. As a result, diagnostic accuracy can be improved, while inter-observer variability is reduced.
The adoption of AI in endoscopy is being driven by the rising global burden of gastrointestinal disorders, including colorectal cancer, and the growing demand for early and accurate diagnosis. Clinical studies have demonstrated that AI-assisted endoscopy can increase adenoma detection rates and support standardized reporting, contributing to improved patient outcomes.
From an operational perspective, AI integration is supporting workflow efficiency by providing real-time alerts, automated documentation, and quality assurance metrics. These capabilities are particularly valuable in high-volume clinical settings, where consistency and speed are critical.
Regulatory approvals and ongoing technological advancements are further strengthening market confidence in AI-based endoscopy platforms. As healthcare systems continue to prioritize precision medicine and digital health solutions, AI in endoscopy is expected to play a central role in shaping the future of minimally invasive diagnostics.
Key Takeaways
- The AI in Endoscopy market recorded revenue of USD 75.9 million in 2024 and is projected to surpass USD 838.9 million by 2033, expanding at a compound annual growth rate (CAGR) of 30.6% over the forecast period.
- Gastrointestinal endoscopy accounted for the largest share among endoscopy types, holding 32.4% of total revenue, supported by its broad clinical use in the diagnosis and treatment of gastrointestinal disorders, including ulcers and cancers.
- Among hardware, software, and services, the services segment led the market with a 39.7% revenue share, driven by increasing dependence on specialized third-party providers for AI system implementation, integration, and maintenance.
- In the CAD analysis category, Computer-Aided Diagnosis (CADx) emerged as the dominant segment, capturing 43.0% of the market, owing to its ability to improve diagnostic precision through advanced analytical algorithms.
- Hospitals represented the leading end-user segment, accounting for 58.2% of market revenue, reflecting their central role in delivering advanced healthcare services and conducting clinical research and medical training.
- North America continued to dominate the global market, securing a 48.7% revenue share, supported by advanced healthcare infrastructure, early adoption of AI technologies, and strong investment in digital health innovation.
Key Performance Statistics
- Esophageal Cancer Detection: AI-enabled endoscopic systems have demonstrated sensitivity levels of up to 98% and specificity approaching 95%. These systems show strong performance in the early detection of esophageal lesions and in accurately assessing the depth of cancer invasion, supporting timely clinical decision-making.
- Early Gastric Cancer (EGC) Identification: AI models used in upper gastrointestinal endoscopy report sensitivity rates as high as 92.2%, enabling effective differentiation between early gastric cancer and benign lesions. This capability facilitates early intervention and improves long-term patient outcomes.
- Helicobacter pylori Detection: AI-based image analysis systems have been shown to outperform conventional endoscopic assessment, achieving sensitivity and specificity values ranging from 85% to 90%. Accurate identification of H. pylori supports gastric cancer risk stratification and prevention strategies.
- Colon Polyp Detection: The integration of AI into colonoscopy procedures has led to a consistent increase in adenoma detection rates (ADR). Multiple clinical studies indicate that AI-assisted detection surpasses standard colonoscopy techniques in identifying precancerous polyps.
- Colorectal Lesion Histopathology Prediction: AI-driven diagnostic tools distinguish between neoplastic and non-neoplastic colorectal polyps with accuracy levels ranging from 78.8% to 94%, supporting more precise clinical diagnoses and optimized treatment planning.
- Capsule Endoscopy (CE) Analysis: AI-supported capsule endoscopy systems achieve greater than 90% sensitivity and specificity in detecting gastrointestinal abnormalities, including bleeding, ulcers, erosions, and mass lesions, significantly enhancing diagnostic efficiency.
- Endoscopy Quality Assurance: AI solutions improve endoscopic quality control by minimizing blind spots, optimizing mucosal visualization, and enhancing inspection consistency. These improvements contribute to higher diagnostic reliability and overall procedural accuracy.
Regional Analysis
North America accounted for a market share of 48.7% in 2023, maintaining its position as the leading contributor to the global market. The region has demonstrated strong and consistent growth over recent years. This dominance can be attributed to well-established healthcare infrastructure, substantial investments in artificial intelligence technologies, and a high rate of adoption of advanced medical solutions. In addition, supportive regulatory frameworks have played a significant role in accelerating the deployment of AI-enabled endoscopy systems, thereby strengthening regional market growth.
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Europe continues to witness steady market expansion, supported by increasing healthcare expenditure and a growing preference for minimally invasive diagnostic and therapeutic procedures. The emphasis on improving clinical outcomes and efficiency across healthcare systems further supports the integration of AI in endoscopy practices.
The Asia-Pacific region is expected to register notable growth during the forecast period. This growth is driven by rising healthcare awareness, expanding healthcare infrastructure, and proactive government initiatives aimed at promoting the adoption of advanced medical technologies. Collectively, these regional trends highlight the varied yet complementary factors influencing the global AI in endoscopy market landscape.
Key Use Cases of AI in Endoscopy
- Enhanced Detection and Diagnostic Accuracy: AI integration in gastrointestinal endoscopy has transformed the detection of preneoplastic lesions, particularly colorectal polyps, while improving the assessment of upper gastrointestinal disorders such as gastroesophageal reflux disease, Barrett’s esophagus, and gastric cancer. Advanced image recognition capabilities support the identification of subtle mucosal abnormalities that may be overlooked during conventional examinations.
- Quality Control and Procedural Optimization: AI-based systems support endoscopists by automatically identifying anatomical landmarks such as the cecum, monitoring withdrawal speed, and accurately measuring polyp size. These features improve procedural safety, efficiency, and clinical decision-making related to polyp management.
- Reduction of Endoscopic Blind Spots: AI-powered platforms, including systems such as WISENSE, significantly reduce blind spot rates during esophagogastroduodenoscopy (EGD) by identifying unexamined gastric regions. This capability is critical for early gastric cancer detection, particularly in settings with limited endoscopic expertise or low disease prevalence.
- Guided and Optical Biopsy Applications: AI enables a transition from random biopsy approaches to targeted tissue sampling through Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems. These tools predict lesion histology in real time, often eliminating the need for physical biopsies while maintaining high sensitivity and specificity.
- Assessment of Tumor Invasion Depth and Margins: AI algorithms analyze endoscopic imaging data to accurately predict tumor invasion depth and lesion boundaries in esophageal and gastric cancers. These precise assessments support individualized treatment planning and improve patient survival outcomes through appropriate therapeutic selection.
Frequently Asked Questions on AI In Endoscopy
- How does AI improve diagnostic accuracy in endoscopy?
Diagnostic accuracy is improved as AI systems analyze endoscopic images pixel by pixel, enabling consistent detection of polyps, tumors, and early-stage abnormalities that may be missed due to operator fatigue or visual limitations. - What types of AI technologies are used in endoscopy?
AI in endoscopy primarily utilizes convolutional neural networks, computer vision, and deep learning models, which are trained on large annotated datasets to recognize patterns associated with gastrointestinal diseases and abnormal tissue structures. - Is AI in endoscopy used in real-time clinical practice?
Real-time clinical use is increasing, as AI systems are integrated directly into endoscopy platforms, providing instant visual alerts and decision support during live procedures without disrupting existing clinical workflows. - What are the limitations of AI in endoscopy?
Limitations include dependence on high-quality training data, variability in performance across patient populations, regulatory approval timelines, and the need for clinician training to effectively interpret AI-generated outputs within clinical contexts. - What factors are driving growth in the AI in endoscopy market?
Market growth is driven by rising gastrointestinal disease prevalence, demand for early cancer detection, technological advancements in medical imaging, and increasing adoption of AI-assisted diagnostic tools in hospitals and ambulatory surgical centers. - How large is the AI in endoscopy market opportunity?
The market opportunity is expanding steadily, supported by growing healthcare digitalization, favorable reimbursement trends in developed regions, and increased investment by medical device manufacturers in AI-enabled endoscopic solutions. - Which end users are adopting AI in endoscopy most rapidly?
Hospitals represent the largest adopters due to high procedure volumes, followed by specialty clinics and ambulatory surgical centers, where AI adoption is driven by efficiency gains, diagnostic consistency, and improved patient outcomes. - What is the future outlook for the AI in endoscopy market?
The future outlook remains cautiously optimistic, with sustained growth expected as AI algorithms mature, regulatory pathways stabilize, and healthcare providers increasingly prioritize precision diagnostics and value-based care delivery models.
Conclusion
The integration of artificial intelligence in endoscopy represents a significant advancement in gastrointestinal diagnostics and interventional care. AI-enabled systems are improving lesion detection, diagnostic accuracy, and procedural consistency while supporting real-time clinical decision-making. Strong clinical evidence, rising gastrointestinal disease prevalence, and increasing demand for early cancer detection are driving market growth.
Operational benefits such as workflow optimization, quality assurance, and automated reporting further strengthen adoption, particularly in high-volume hospitals. With continued technological innovation, regulatory support, and investment in digital health, AI in endoscopy is positioned to become a core component of precision, minimally invasive diagnostics worldwide.
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