The integration of artificial intelligence (AI) into endoscopy is advancing diagnostic accuracy and efficiency. AI has become a valuable tool in gastrointestinal (GI) endoscopy, offering consistent and rapid diagnostic support through data analysis.1
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Endoscopy, while invaluable for the diagnosis and treatment of GI disorders, remains highly dependent on the experience of the endoscopist and the complexity of examinations. Variability in accuracy remains a challenge.2
AI-driven techniques, including deep learning models such as convolutional neural networks (CNNs), address these challenges by enhancing endoscopic capabilities. These technologies support early detection of conditions like GI cancers, improve personalized treatment strategies, and contribute to better patient outcomes.2
AI Applications in Endoscopy
Image Analysis and Lesion Detection
AI algorithms are enhancing image analysis in GI endoscopy, contributing to lesion detection, characterization, and real-time decision support during procedures.
Gastric precancerous lesions and Helicobacter pylori (H. pylori) infections, which are critical risk factors for gastric cancer (GC), have been a focus of AI-driven advancements.2 By leveraging computational power and machine learning capabilities, AI enhances the precision and efficiency of diagnostic workflows.
In lesion detection, AI has demonstrated superior accuracy compared to traditional endoscopic methods. For instance, findings by Tang et al. demonstrated that AI outperformed endoscopists in identifying early-stage gastric cancer, highlighting its potential to enhance early diagnosis and inform treatment strategies.3
AI also excels in providing real-time decision support during procedures. By integrating AI into endoscopic workflows, clinicians gain immediate, data-driven insights that support timely and informed interventions.4
Machine learning techniques, such as the XGBoost classifier, have shown effectiveness in predicting risk factors and infection statuses, outperforming traditional statistical models. For instance, in a study involving school children in Ethiopia, AI proved instrumental in forecasting H. pylori infections based on risk factors, showcasing its versatility and scalability.4
Monash University and Optiscan Imaging Ltd have recently collaborated to develop a next-generation GI flexible endomicroscope powered by edge-AI technology. This innovative system, led by Associate Professor Zongyuan Ge and Dr. Yasmeen George, incorporates a miniature digital microscope that fits standard endoscope biopsy channels, enabling real-time subcellular imaging and automated detection of abnormal cells.5
The AI platform is designed to reduce cancer screening times, enhance diagnostic accuracy, and improve patient outcomes. Supported by an Australian CRC-P grant, this collaboration combines cutting-edge AI research and industry innovation to advance GI disease diagnosis and treatment.5
Workflow Automation and Reduced Workload
AI is increasingly automating tasks such as video review and annotation, streamlining workflows, and reducing clinician workload. Its ability to rapidly process and analyze large volumes of data has been demonstrated across various medical imaging applications, improving both accuracy and productivity.6
For instance, AI-powered CNNs have matched the sensitivity of experienced radiologists and identified overlooked cases, such as detecting 8.4 % of missed lung nodules in complex cases.7 Similarly, in dental imaging, AI-supported systems have reduced the time required for image interpretation, demonstrating its potential to accelerate diagnostic workflows.8
Clinical Insights
Recent studies highlight the role of AI in improving detection rates of colorectal cancer (CRC) and GI disorders, addressing challenges such as polyp detection and disease monitoring. CRC, the third most common cancer worldwide, benefits from AI systems that enhance adenoma detection rates (ADR), a key indicator of colonoscopy quality.1
Traditional colonoscopy methods have a 20–30 % miss rate for colon polyps, increasing the risk of post-colonoscopy CRC. AI-assisted colonoscopy, with real-time polyp detection, has significantly improved ADR and the detection of adenomas, including diminutive adenomas.1
AI’s ability to classify polyps with high accuracy helps reduce unnecessary medical interventions. For instance, systems like those developed by Mori et al. predict polyp pathology and support a “diagnose-and-leave” strategy for non-neoplastic polyps. Advanced models by Byrne et al. achieve up to 94 % accuracy in differentiating adenomatous from hyperplastic polyps, comparable to expert colonoscopists.9
AI also shows promise in managing inflammatory bowel disease (IBD), a chronic inflammatory GI disorder. Recent models by Maeda et al. and Takenaka et al. predict persistent histological inflammation in ulcerative colitis (UC) patients with high accuracy, reducing the reliance on invasive biopsies.10,11 These models reliably assess disease activity, improving treatment decisions and long-term management strategies.
AI has a significant impact on endoscopy training and education, offering tools for skill enhancement and performance evaluation. By monitoring factors such as endoscopy thoroughness and lesion detection rates, AI supports consistent quality control.1
For example, systems developed by Wu et al. improve thoroughness during upper GI endoscopy, significantly reducing blind spot rates.12 Similarly, AI applications by Su et al. enhance adenoma detection rates by analyzing withdrawal timing and inspection stability during colonoscopies.13
AI systems also assist junior physicians in clinical practice by offering automated guidance, targeted biopsy support, and detailed performance feedback. These applications improve diagnostic accuracy and reduce variability among practitioners.
Benefits and Challenges
AI in endoscopy provides benefits such as improved diagnostic accuracy, reduced variability in interpretations, and enhanced patient outcomes. By efficiently analyzing large datasets and identifying anomalies, AI increases precision and accelerates diagnoses. It also supports personalized treatment planning, enabling tailored strategies that improve the quality of care.14
Additionally, AI reduces variability by standardizing interpretations and minimizing human error. These advancements not only enhance patient safety and engagement but also streamline workflows, lower healthcare costs, and extend access to underserved areas, ultimately transforming the quality and efficiency of care.14
Despite its benefits, the integration of AI into endoscopy faces several challenges, such as data privacy concerns, requiring stringent security measures to protect patient information. The development of AI models also depends on large, diverse, and annotated datasets, as limited data diversity can result in algorithmic bias and reduced generalizability.6
Integrating AI into clinical workflows also requires collaboration between developers and healthcare professionals to ensure smooth adoption, proper training, and alignment with clinical needs. These efforts are essential to maintain the quality of patient care while leveraging AI tools effectively.6
Future Directions
The future of AI in endoscopy is bright, driven by advancements in machine learning, predictive analytics, and personalized medicine. Refined algorithms promise greater precision and efficiency, while predictive analytics could anticipate disease progression for proactive interventions. AI’s growing role in personalized medicine will further tailor diagnostics and treatments to individual patient needs, optimizing care outcomes.1,14
Integration of AI with other cutting-edge technologies, such as robotics and augmented reality, could transform procedural capabilities. For example, augmented reality overlays could guide endoscopists during complex procedures, further enhancing precision.1-2
As AI continues to evolve, collaborations between clinicians, engineers, and regulatory bodies will be critical to ensuring ethical and impactful applications.
More from AZoOptics: Colon Capsule Endoscopy: The Future of Non-Invasive Colorectal Exams
References and Further Reading
1. Guo, F., Meng, H. (2024). Application of Artificial Intelligence in Gastrointestinal Endoscopy. Arab Journal of Gastroenterology. https://www.sciencedirect.com/science/article/pii/S168719792300120X
2. Ali, H.; Muzammil, MA.; Dahiya, DS.; Ali, F.; Yasin, S.; Hanif, W.; Gangwani, MK.; Aziz, M.; Khalaf, M.; Basuli, D. (2024). Artificial Intelligence in Gastrointestinal Endoscopy: A Comprehensive Review. Annals of Gastroenterology. https://pubmed.ncbi.nlm.nih.gov/38481787/
3. Tang, D.; Wang, L.; Ling, T.; Lv, Y.; Ni, M.; Zhan, Q.; Fu, Y.; Zhuang, D.; Guo, H.; Dou, X. (2020). Development and Validation of a Real-Time Artificial Intelligence-Assisted System for Detecting Early Gastric Cancer: A Multicentre Retrospective Diagnostic Study. EBioMedicine. https://pubmed.ncbi.nlm.nih.gov/33254026/
4. Tran, V.; Saad, T.; Tesfaye, M.; Walelign, S.; Wordofa, M.; Abera, D.; Desta, K.; Tsegaye, A.; Ay, A.; Taye, B. (2022). Helicobacter Pylori (H. Pylori) Risk Factor Analysis and Prevalence Prediction: A Machine Learning-Based Approach. BMC Infectious Diseases. https://pubmed.ncbi.nlm.nih.gov/33254026/
5. Monash University (2024). AI-powered endoscopes to help reduce cancer screening times. [Online] Medianet. Available at: https://newshub.medianet.com.au/2024/11/ai-powered-endoscopes-to-help-reduce-cancer-screening-times/75198/
6. Khalifa, M.; Albadawy, M. (2024). Ai in Diagnostic Imaging: Revolutionising Accuracy and Efficiency. Computer Methods and Programs in Biomedicine Update. https://www.sciencedirect.com/science/article/pii/S2666990024000132
7. Abadia, AF.; Yacoub, B.; Stringer, N.; Snoddy, M.; Kocher, M.; Schoepf, UJ.; Aquino, GJ.; Kabakus, I.; Dargis, D.; Hoelzer, P. (2022). Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients with Complex Lung Disease: A Noninferiority Study. Journal of thoracic imaging. https://pubmed.ncbi.nlm.nih.gov/34387227/
8. Schwendicke, F.; Mertens, S.; Cantu, AG.; Chaurasia, A.; Meyer-Lueckel, H.; Krois, J. (2022). Cost-Effectiveness of Ai for Caries Detection: Randomized Trial. Journal of dentistry. https://pubmed.ncbi.nlm.nih.gov/35245626/
9. Mori, Y.; Kudo, S.-e.; Misawa, M.; Saito, Y.; Ikematsu, H.; Hotta, K.; Ohtsuka, K.; Urushibara, F.; Kataoka, S.; Ogawa, Y. (2018). Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Annals of internal medicine. https://pubmed.ncbi.nlm.nih.gov/30105375/
10. Takenaka, K.; Ohtsuka, K.; Fujii, T.; Negi, M.; Suzuki, K.; Shimizu, H.; Oshima, S.; Akiyama, S.; Motobayashi, M.; Nagahori, M. (2020). Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images from Patients with Ulcerative Colitis. Gastroenterology. https://pubmed.ncbi.nlm.nih.gov/32060000/
11. Maeda, Y.; Kudo, S.-e.; Mori, Y.; Misawa, M.; Ogata, N.; Sasanuma, S.; Wakamura, K.; Oda, M.; Mori, K.; Ohtsuka, K. (2019). Fully Automated Diagnostic System with Artificial Intelligence Using Endocytoscopy to Identify the Presence of Histologic Inflammation Associated with Ulcerative Colitis (with Video). Gastrointestinal endoscopy. https://pubmed.ncbi.nlm.nih.gov/30268542/
12. Wu, L.; Zhang, J.; Zhou, W.; An, P.; Shen, L.; Liu, J.; Jiang, X.; Huang, X.; Mu, G.; Wan, X. (2019). Randomised Controlled Trial of Wisense, a Real-Time Quality Improving System for Monitoring Blind Spots During Esophagogastroduodenoscopy. Gut. https://gut.bmj.com/content/68/12/2161
13. Su, J.-R.; Li, Z.; Shao, X.-J.; Ji, C.-R.; Ji, R.; Zhou, R.-C.; Li, G.-C.; Liu, G.-Q.; He, Y.-S.; Zuo, X.-L. (2020). Impact of a Real-Time Automatic Quality Control System on Colorectal Polyp and Adenoma Detection: A Prospective Randomized Controlled Study (with Videos). Gastrointestinal endoscopy. https://gut.bmj.com/content/68/12/2161
14. Mukherjee, S.; Vagha, S.; Gadkari, P. (2024). Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer. Cureus. https://pubmed.ncbi.nlm.nih.gov/38510911/
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