Artificial Intelligence (AI) in Endoscopy

Global Artificial Intelligence (AI) in Endoscopy Market to Reach US$115.8 Billion by 2030

The global market for Artificial Intelligence (AI) in Endoscopy estimated at US$20.7 Billion in the year 2024, is expected to reach US$115.8 Billion by 2030, growing at a CAGR of 33.2% over the analysis period 2024-2030. Flexible Endoscopes, one of the segments analyzed in the report, is expected to record a 35.7% CAGR and reach US$87.1 Billion by the end of the analysis period. Growth in the Rigid Endoscopes segment is estimated at 27.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$5.4 Billion While China is Forecast to Grow at 31.4% CAGR

The Artificial Intelligence (AI) in Endoscopy market in the U.S. is estimated at US$5.4 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$17.2 Billion by the year 2030 trailing a CAGR of 31.4% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 30.7% and 28.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 22.8% CAGR.

Global Artificial Intelligence (AI) in Endoscopy Market – Key Trends & Drivers Summarized

Why Is AI Revolutionizing the Field of Endoscopy in Clinical Diagnostics?

Artificial Intelligence (AI) is reshaping the landscape of endoscopy by enabling more accurate, real-time diagnostics and improving the efficiency of clinical procedures. Endoscopy, which allows visualization of internal organs such as the gastrointestinal (GI) tract, has long been a critical tool for diagnosing conditions like colorectal cancer, ulcers, inflammatory bowel disease, and gastrointestinal bleeding. However, traditional endoscopic examinations are subject to human limitations, including operator fatigue, variability in experience, and the possibility of missing subtle abnormalities. AI, particularly through machine learning and computer vision technologies, is enhancing these procedures by providing real-time image analysis that assists clinicians in detecting polyps, lesions, and other abnormalities that may go unnoticed. AI systems can process high-resolution endoscopic video streams and highlight areas of concern, allowing for immediate review and improved decision-making. Moreover, AI-driven software can quantify lesion sizes, assess tissue characteristics, and even suggest the likelihood of malignancy, thus reducing reliance on subjective interpretation. These systems are especially valuable in early cancer detection, where small improvements in sensitivity can lead to significantly better patient outcomes. Additionally, AI tools help streamline documentation, annotate findings, and ensure adherence to procedural protocols, enhancing overall workflow efficiency. As more hospitals adopt digital imaging platforms, the integration of AI becomes a logical extension, enabling clinicians to leverage the full value of captured visual data. With growing demand for minimally invasive procedures and pressure to improve diagnostic accuracy while reducing procedure times, AI is proving to be a game-changing technology in modern endoscopy.

How Are Advancements in Computer Vision and Deep Learning Transforming AI Applications in Endoscopy?

Rapid progress in computer vision, deep learning, and neural networks is accelerating the evolution of AI applications in endoscopy, driving capabilities that were unimaginable just a few years ago. These technologies allow systems to interpret endoscopic images and video feeds with a level of precision that often rivals or even exceeds that of experienced specialists. Convolutional neural networks (CNNs), in particular, have demonstrated remarkable success in image classification tasks, making them ideal for distinguishing between normal and abnormal tissues, identifying small or flat polyps, and detecting early-stage lesions that may indicate malignancies. Advanced algorithms can now segment images into regions of interest, flag potential pathologies in real time, and track anomalies throughout a procedure to ensure comprehensive examination. These models are trained on massive datasets collected from thousands of prior procedures, enabling the AI to learn complex patterns and continuously improve its accuracy. Some platforms also integrate natural language processing (NLP) to automatically generate procedural reports, reducing the documentation burden for physicians. Furthermore, AI systems are being designed with feedback mechanisms that allow clinicians to accept or reject suggestions, creating a dynamic learning loop that enhances both the AI and the physician`s performance. Companies are also working on integrating augmented reality into endoscopy, where AI overlays visual cues directly on the live feed to assist with navigation and targeting. These innovations are not only improving diagnostic confidence and consistency but also expanding the role of AI into therapeutic endoscopy, where precision guidance can assist in resection, ablation, and stent placement. As algorithms grow more sophisticated and regulatory approvals increase, the technological synergy between AI and endoscopy is expected to redefine the standard of care in diagnostic medicine.

How Are Healthcare Systems, Training Needs, and Regulatory Bodies Influencing Market Adoption?

The adoption of AI in endoscopy is being shaped by the interplay of healthcare system priorities, clinician training needs, and evolving regulatory frameworks. Healthcare providers are under increasing pressure to deliver faster, more accurate diagnostics while containing costs and reducing the burden on clinicians. AI-enabled endoscopy offers a compelling solution by enhancing diagnostic accuracy, improving efficiency, and potentially reducing the need for repeat procedures. Hospitals and diagnostic centers are thus beginning to invest in AI-integrated endoscopic platforms as part of broader digital transformation initiatives. However, the success of these investments depends significantly on clinician trust and acceptance. To bridge this gap, medical institutions are incorporating AI training into gastroenterology fellowship programs and continuing education for practicing endoscopists. These programs focus on interpreting AI-generated outputs, understanding the limitations of algorithmic suggestions, and maintaining clinical judgment. On the regulatory side, agencies such as the FDA and the European Medicines Agency are working to establish clear guidelines for AI-based diagnostic tools. These regulations include rigorous performance benchmarks, transparency requirements, and post-market surveillance mandates to ensure patient safety and clinical efficacy. In response, manufacturers are conducting large-scale multicenter trials to validate the real-world performance of AI systems across different populations and healthcare settings. Reimbursement policies are also beginning to evolve, with some payers recognizing the cost-effectiveness of AI-assisted diagnostics in preventing advanced-stage diseases. The combination of educational support, regulatory clarity, and economic incentives is helping to build a favorable environment for the widespread adoption of AI in endoscopy. These systemic developments are essential for ensuring that AI tools are not only available but also reliably integrated into routine clinical workflows.

What Is Driving the Continued Growth of the Global AI in Endoscopy Market?

The growth in the artificial intelligence in endoscopy market is driven by several interrelated forces, including the rising incidence of gastrointestinal diseases, the demand for early cancer detection, technological advancements in imaging systems, and the global push toward precision medicine. Gastrointestinal cancers, particularly colorectal cancer, remain among the most common and deadly forms of cancer worldwide, yet they are highly preventable and treatable if detected early. AI tools that enhance the detection of precancerous polyps during colonoscopy have the potential to significantly improve screening outcomes and reduce mortality. This clinical value proposition is fueling investment from both private and public sectors, with major funding directed toward AI research, development, and integration within healthcare systems. The transition from analog to digital endoscopic systems has also laid the groundwork for AI deployment, providing the data infrastructure necessary to support machine learning models. Simultaneously, global demographic shifts, including aging populations and increasing healthcare access in emerging markets, are expanding the demand for endoscopic procedures. This creates a scalable opportunity for AI to support overburdened healthcare systems by improving throughput and reducing diagnostic errors. Furthermore, the entry of tech giants and medtech innovators into the field is accelerating product innovation, competitive pricing, and wider availability. Cross-disciplinary collaboration between gastroenterologists, data scientists, and software engineers is leading to the development of specialized AI tools that are tailored to specific diagnostic needs and clinical environments. With growing evidence of clinical benefit, regulatory momentum, and integration with broader health information systems, the AI in endoscopy market is poised for sustained and dynamic growth. It represents a crucial convergence of technological advancement and medical necessity that is reshaping diagnostic medicine for the better.

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence (AI) in Endoscopy market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Type (Flexible Endoscopes, Rigid Endoscopes); Technique (Autofluorescence Endoscopy, Confocal Endoscopy, Endoscopic Ultrasound, Capsule Endoscopy); Imaging Modality (White-Light Endoscopy, Fluorescence Endoscopy, Narrow-Band Imaging, High-Definition Endoscopy); Application (Gastrointestinal Endoscopy Application, Urological Endoscopy Application, Respiratory Endoscopy Application, Colonoscopy Application)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 42 Featured) -

• Arthrex Inc.
• Aspen Surgical Products
• B. Braun Melsungen AG
• Canon Medical Systems
• Carl Zeiss Meditec AG
• CureMetrix
• CyberMDX
• D2S, Inc.
• DeepMind Technologies
• Fujifilm Holdings
• GE Healthcare
• Intuitive Surgical
• Johnson & Johnson Medical
• Medtronic
• Olympus Corporation
• Reach Surgical
• Stryker Corporation
• Teleflex Incorporated
• VGI Health Technology
• Vicon Motion Systems

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