Table of Contents
Overview
New York, NY – Jan 23, 2026 – The Global AI In Endoscopy Market size is expected to be worth around USD 838.9 Million by 2033 from USD 58.1 Million in 2023, growing at a CAGR of 30.6% during the forecast period from 2024 to 2033.
The integration of artificial intelligence (AI) in endoscopy is transforming gastrointestinal diagnostics by improving accuracy, efficiency, and clinical outcomes. AI-enabled endoscopy systems are designed to assist clinicians in real time by enhancing lesion detection, characterization, and documentation during procedures.
AI algorithms, particularly those based on deep learning, are trained on large datasets of endoscopic images and videos. These systems can automatically identify subtle mucosal abnormalities, including polyps and early-stage cancers, which may be difficult to detect with conventional visual inspection. As a result, diagnostic consistency is improved, and the risk of missed lesions is reduced.
The adoption of AI in endoscopy is driven by the growing burden of gastrointestinal diseases and the increasing demand for early and accurate diagnosis. AI-assisted tools support clinicians by reducing inter-observer variability and optimizing workflow efficiency. Procedure times can be streamlined, while reporting accuracy and standardization are enhanced.
From a healthcare system perspective, AI in endoscopy contributes to improved patient outcomes and potential cost savings. Early detection of conditions such as colorectal cancer enables timely intervention, which is associated with better survival rates and reduced treatment costs. Additionally, AI-based quality control features help ensure adherence to clinical guidelines.
Ongoing technological advancements and supportive regulatory developments are expected to accelerate the adoption of AI in endoscopy across hospitals and diagnostic centers. As clinical validation expands, AI is anticipated to become an integral component of routine endoscopic practice, supporting clinicians in delivering high-quality, data-driven care.
Key Takeaways
- The AI in Endoscopy market recorded revenue of USD 58.1 million in 2023 and is projected to surpass USD 838.9 million by 2033, expanding at a CAGR of 30.6% during the forecast period.
- Gastrointestinal endoscopy was identified as the leading endoscopy type, accounting for 32.4% of total revenue, driven by its broad clinical use in the diagnosis and management of gastrointestinal disorders, including ulcers and cancers.
- Among hardware, software, and services, the services segment held the largest share at 39.7%, supported by the increasing dependence on third-party providers for AI system implementation, maintenance, and optimization.
- In the CAD analysis category, CADx (Computer-Aided Diagnosis) dominated the market with a 43.0% revenue share, supported by its ability to improve diagnostic precision through advanced algorithmic analysis.
- Hospitals represented the largest end-user segment, capturing 58.2% of the market, reflecting their central role in healthcare delivery, advanced diagnostics, and clinical research activities.
- North America continued to lead the global market, securing a revenue share of 48.7%, supported by strong healthcare infrastructure and early adoption of AI-enabled medical technologies.
Market Segmentation Analysis
- Type Analysis: Gastrointestinal endoscopy dominated with 32.4% revenue share, driven by broad diagnostic and therapeutic applications, early disease detection, technological advancements, preventive screening adoption, and rising gastrointestinal disorder prevalence globally worldwide demand.
- Component Analysis: Services led with 39.7% market share, supported by growing dependence on consulting, implementation, training, and support services, as organizations seek expertise to deploy, manage, and optimize complex AI systems effectively.
- Type of CAD Analysis: CADx held a 43.0% share, driven by advanced algorithms enhancing image interpretation, diagnostic accuracy, and workflow efficiency across X-ray, MRI, and CT modalities, supporting early detection and improved clinical decision-making.
- End-User Analysis: Hospitals accounted for 58.2% share, reflecting their central role in delivering comprehensive care, advanced procedures, emergency services, research, and education, supported by robust infrastructure and high patient volumes globally sustained.
Regional Analysis
North America accounted for a dominant market share of 48.7% in 2023 and has consistently remained the leading contributor to the AI in endoscopy market. Strong growth across recent years has been supported by the region’s well-established healthcare infrastructure and substantial investments in artificial intelligence technologies. High adoption of advanced medical solutions, combined with supportive regulatory frameworks, has continued to strengthen market expansion across the region.
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In Europe, market growth is being supported by increasing healthcare expenditure and a rising preference for minimally invasive diagnostic and therapeutic procedures. The region’s focus on improving clinical outcomes and healthcare efficiency is further encouraging the adoption of AI-enabled endoscopic solutions.
The Asia-Pacific region is expected to witness notable growth over the forecast period, driven by rising healthcare awareness, expanding healthcare infrastructure, and increasing government initiatives aimed at promoting advanced medical technologies. Collectively, these regional trends highlight the varied yet complementary factors influencing the global AI in endoscopy market and underline its sustained growth potential across developed and emerging economies.
Emerging trends in AI in endoscopy
1. Real-time polyp detection (CADe) is moving into routine screening. Meta-analysis of 28 randomized trials (23,861 patients) showed a 20% higher adenoma detection rate and a 55% lower adenoma miss rate versus standard colonoscopy.
2. Cloud delivery is emerging so AI can be updated centrally and shared across rooms. Olympus’ Odin Medical reported FDA 510(k) clearance for CADDIE, described as the first cloud-based AI system for polyp detection in colonoscopy.
3. Quality-control AI is expanding beyond finding polyps to measuring procedure technique. A JAMA Network Open study reported an automatic quality control system increased adenoma detection rate by 10.1 percentage points compared with standard colonoscopy.
4. AI is shifting from “detect” to “characterize” to reduce unnecessary removals. Network meta-analyses compare CADe and CADx, using pooled sensitivity and specificity to benchmark systems and support guideline-style performance thresholds.
5. Upper-GI AI is accelerating, especially for Barrett’s neoplasia support. Deep-learning studies report strong performance; one review cites about 90% accuracy, with reported sensitivities around 71–72% in certain video and biopsy datasets.
Use cases of AI in endoscopy
1. During live colonoscopy, AI marks suspected polyps on the video feed, prompting re-inspection. In a matched cohort (474 vs 474), adenoma detection rose from 26.4% to 35.9%, and adenomas per colonoscopy increased 0.43 to 0.69.
2. AI works as a “second reader” to reduce missed lesions and standardize results. Across 28 randomized trials, adenoma miss rate fell 55% (risk ratio 0.45), supporting use in population screening.
3. Procedure-quality AI tracks mucosal coverage and blind spots, then alerts the endoscopist in real time. One automatic quality control system increased adenoma detection by 10.1 percentage points versus standard practice, supporting training and audit.
4. In Barrett’s surveillance, AI highlights suspicious mucosa to guide targeted biopsy and therapy planning. Results report about 90% diagnostic accuracy, with sensitivities around 71–72% in some video and biopsy datasets, helping earlier neoplasia recognition.
5. Cloud AI lets hospitals deploy detection across many rooms without major hardware upgrades and enables centralized updates. Olympus’ Odin Medical reported FDA 510(k) clearance for CADDIE, supporting cloud-assisted polyp detection during colonoscopy.
Frequently Asked Questions on AI In Endoscopy
- How does AI improve endoscopic diagnosis?
AI enhances endoscopic diagnosis by analyzing real-time video frames to identify subtle lesions, polyps, or bleeding sites that may be missed by the human eye, thereby supporting earlier detection and improved clinical decision-making. - What types of AI technologies are used in endoscopy?
AI in endoscopy primarily uses deep learning, convolutional neural networks, and computer vision algorithms to process high-resolution endoscopic images, enabling automated lesion detection, classification, and quality assessment during diagnostic and therapeutic procedures. - Which clinical areas benefit most from AI-enabled endoscopy?
Gastrointestinal oncology, particularly colorectal cancer screening, benefits significantly from AI-enabled endoscopy, as these systems improve adenoma detection rates and help standardize examination quality across operators and healthcare settings. - What are the key limitations of AI in endoscopy?
Key limitations include dependence on high-quality training datasets, integration challenges with existing endoscopic platforms, regulatory approval timelines, and the need for clinician training to ensure appropriate interpretation and trust in AI-assisted outputs. - What factors are driving market adoption?
Market adoption is driven by demand for improved diagnostic accuracy, reduced miss rates, workflow efficiency, supportive regulatory developments, and increasing investments by endoscopy device manufacturers and healthcare providers in AI-based clinical solutions. - Which end users are adopting AI in endoscopy solutions?
Hospitals, specialty gastroenterology clinics, and ambulatory surgical centers are the primary end users, as these facilities handle high procedure volumes and increasingly prioritize quality metrics, early disease detection, and value-based healthcare outcomes. - What is the future outlook for the AI in endoscopy market?
The future outlook remains cautiously optimistic, with continued advancements in algorithm accuracy, expanding clinical indications, and integration with robotic and cloud-based platforms expected to support sustained market expansion over the forecast period.
Conclusion
AI integration in endoscopy is reshaping gastrointestinal diagnostics by enhancing detection accuracy, procedural quality, and clinical efficiency. Deep learning–based systems have demonstrated measurable improvements in adenoma detection rates, reduced miss rates, and greater diagnostic consistency across operators. Market growth is being supported by rising gastrointestinal disease burden, expanding screening programs, and increasing adoption by hospitals.
Technological advances such as real-time CADe/CADx, quality-control analytics, and cloud-based deployment are accelerating clinical uptake. As regulatory approvals and clinical validation expand, AI is expected to become a standard component of endoscopic practice, supporting data-driven, high-quality, and cost-effective patient care globally.
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