Current Quality Metrics in Colonoscopy
High-quality colonoscopy remains the cornerstone of colorectal cancer prevention, and multiple performance indicators are used to benchmark exam effectiveness. Among the most important is the adenoma detection rate (ADR), defined as the proportion of screening colonoscopies in which at least one adenoma is found. ADR is strongly linked to post-colonoscopy colorectal cancer risk; each 1% increase in ADR corresponds to a 3% reduction in cancer incidence. Other commonly tracked metrics include adenomas per colonoscopy (APC), which provides a more granular view of endoscopist performance, particularly relevant in the era of enhanced imaging and polyp detection tools. Withdrawal time, the duration spent examining the colon after reaching the cecum, is another key factor. Studies suggest a minimum of 6 to 10 minutes is associated with higher ADRs.
Despite these benchmarks, real-world variability remains high between endoscopists and across institutions. Flat lesions, poor prep, and operator fatigue can all contribute to missed pathology. This performance gap has driven interest in artificial intelligence (AI)-assisted technologies, including CADe (computer-aided detection) and CADx (computer-aided diagnosis), which aim to improve consistency, enhance lesion recognition, and support clinical decision-making in real time.
How Real-Time CADe Works (Technical Overview)
Computer-aided detection (CADe) systems are designed to assist endoscopists by highlighting potential polyps in real time during colonoscopy. These tools use deep learning algorithms, particularly convolutional neural networks (CNNs), trained on vast libraries of annotated endoscopy video frames to distinguish polyps from normal mucosa with high sensitivity.
The typical CADe pipeline involves live video capture from the endoscope, which is then fed through the AI model for frame-by-frame analysis. When the system identifies a suspicious region, based on shape, color, texture, or motion, it draws a bounding box overlay on the monitor, prompting the endoscopist to further inspect the site. The latency from frame acquisition to detection display is typically under 100 milliseconds, enabling seamless workflow integration without perceptible delay.
CADe systems are either hardware-integrated into modern endoscopy processors or cloud-connected, allowing software updates and data logging. Most are optimized for high-definition white-light imaging, though some are expanding into narrow-band or texture-enhanced modes.
Detection performance is particularly strong for small, diminutive, or sessile polyps, which are most frequently missed during routine exams. However, current systems still face challenges with flat lesions, stool or bubble interference, and ambiguous mucosal patterns.
The key advantage of CADe is its consistency: unlike humans, the algorithm does not tire or lose vigilance. As such, it serves as a “second observer”, improving baseline detection and standardizing quality across providers and practice settings.
Latest Evidence on ADR Gains and False Positive Burden
A growing body of clinical evidence supports the use of CADe systems in enhancing adenoma detection rates (ADR), a key quality indicator in colonoscopy. In a 2023 randomized controlled trial published in The Lancet Gastroenterology & Hepatology, CADe raised ADR from 39% to 54%, primarily by improving detection of diminutive and flat adenomas, especially in the right colon. These small lesions, while often overlooked, can carry significant neoplastic potential.
A 2025 American Journal of Gastroenterology systematic review and meta-analysis further confirmed that CADe tools reduce the adenoma miss rate (AMR) by nearly 50%, particularly in high-risk patients or those with suboptimal bowel preparation. Improvements were seen across different endoscopists, suggesting that CADe narrows inter-operator variability and elevates overall exam quality.
However, the technology is not without trade-offs. False positives, most commonly due to stool, folds, or mucosal glare, can result in increased withdrawal times, unnecessary biopsies, and operator distraction. In some studies, endoscopists report a higher cognitive load and occasional “alert fatigue,” particularly in high-volume sessions. To mitigate these concerns, some systems incorporate confidence scores, time-based filters, or dual-frame validation to suppress transient artifacts. Ongoing algorithm refinement seeks to balance sensitivity with clinical specificity, preserving the gains in lesion detection without overburdening the user.
As the evidence base matures, the net benefit of CADe, particularly in routine screening populations, appears robust, with ADR improvements likely translating into long-term reductions in colorectal cancer incidence.
CADx for Optical Histology: ‘Resect and Discard’ Potential (≈200 words)
Computer-aided diagnosis (CADx) extends AI’s utility by classifying detected polyps in real time as neoplastic or non-neoplastic, supporting decisions to either resect and discard or leave in situ. This approach could significantly reduce the need for pathology review of diminutive polyps, especially in the rectosigmoid colon, where non-neoplastic lesions are common.
CADx systems are trained on high-resolution video from techniques such as narrow-band imaging (NBI) and endocytoscopy, learning subtle vascular and surface patterns that correlate with histology. Some models have already demonstrated diagnostic accuracy approaching the PIVI (Preservation and Incorporation of Valuable endoscopic Innovations) thresholds, which set benchmarks for safe optical biopsy.
While early results are promising, current clinical use remains limited due to regulatory, medico-legal, and confidence barriers. As CADx matures, it may enable cost-effective, real-time histologic triage, reducing burden on pathology labs and shortening post-procedure decision cycles.
Cost, Deskilling Concerns, and Medico-Legal Aspects
The adoption of CADe and CADx systems brings both promise and practical challenges, particularly regarding cost, workforce impact, and liability.
Financial considerations are significant. AI-assisted systems often require proprietary software licenses, hardware upgrades, cloud storage, and integration with endoscopy suites — investments that may not be reimbursed by all payers. Long-term cost-effectiveness depends on reductions in missed lesions, fewer repeat exams, and more efficient pathology use.
Deskilling is another concern. Overreliance on AI could erode the pattern recognition skills of endoscopists, especially trainees, potentially diminishing operator independence. Incorporating AI should not replace foundational training but complement it with proper credentialing.
The medico-legal landscape remains underdeveloped. Questions persist around responsibility when an AI system fails to flag a lesion. Should liability rest with the physician, the institution, or the software provider? As of 2025, most systems are approved as “decision-support tools,” meaning the endoscopist remains accountable. Documentation of AI detections, integration into reports, and consistent informed consent protocols are necessary to protect both clinicians and patients in the evolving legal framework.
Implementation Checklist for Endoscopy Units
Successfully integrating AI-assisted systems like CADe and CADx into clinical practice requires strategic planning, infrastructure readiness, and staff engagement. Below is a practical framework for endoscopy units considering adoption:
1. Technical Compatibility: Assess compatibility with existing endoscopy platforms and processors. Some CADe tools require specific video outputs or proprietary imaging systems. If cloud-based, ensure stable, high-speed internet connectivity and secure data pathways.
2. Vendor Selection and Trialing: Evaluate multiple vendors based on accuracy, regulatory approvals, user interface, and integration with electronic health records. Many centers begin with pilot programs to benchmark performance against internal ADR/APC baselines.
3. Staff Training and Workflow Integration: Train endoscopists, nurses, and techs on system use, response to AI prompts, and recognition of false positives. AI overlays must be interpreted in context, not blindly accepted.
4. Data Capture and Quality Audit: Establish processes for recording AI detections in procedure reports and linking to pathology. Use early implementation phases to track ADR changes, detection patterns, and staff feedback. Consider integrating AI performance into ongoing quality assurance reviews.
5. Reimbursement and Business Case: Analyze local and national reimbursement pathways. Currently, AI use is not uniformly billable, but long-term savings may result from fewer missed lesions and reduced downstream costs. Build a cost-benefit justification for stakeholders and procurement.
6. Legal and Ethical Protocols: Implement policies on informed consent, error documentation, and liability roles. Ensure patients are aware that AI is being used as a diagnostic adjunct, not as a replacement for physician judgment.
Properly managed, AI tools can significantly enhance procedural quality and safety while supporting equitable outcomes across diverse patient populations.
Key Practice Points
- CADe systems have consistently demonstrated improved adenoma detection rates (ADR), particularly for diminutive and flat lesions, enhancing overall exam quality.
- CADx tools are showing promise for real-time optical histology, supporting “resect and discard” strategies for diminutive polyps in selected settings.
- Successful implementation requires balancing cost, staff training, medico-legal clarity, and an evidence-based approach to workflow integration.
References
1. Tang, L., Zhang, Y., Wang, X., et al. (2025). Artificial intelligence-assisted colonoscopy and missed lesion reduction: A systematic review. American Journal of Gastroenterology. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175867/
2. Hassan, C., Spadaccini, M., Iacucci, M., et al. (2023). Real-time CADe in colonoscopy: A randomized trial. The Lancet Gastroenterology & Hepatology. https://www.thelancet.com/journals/langas/article/PIIS2468-1253(23)00104-8
3. Kim, J. H., Lee, D. H., Patel, S. G., et al. (2025). Multicentre evaluation of AI-assisted colonoscopy: Performance and workflow impact. Gastrointestinal Endoscopy. https://www.giejournal.org/article/S0016-5107(24)03471-0/fulltext
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5. Hassan, C., East, J. E., Mori, Y., Radaelli, F., Spada, C., & Rex, D. K. (2022). Artificial intelligence-assisted colonoscopy: A joint European Society of Gastrointestinal Endoscopy (ESGE)/European Society of Gastrointestinal and Abdominal Radiology (ESGAR)/European Society of Digestive Oncology (ESDO) position statement. Endoscopy, 54(01), 33–49. https://doi.org/10.1055/a-1661-7315
6. Brand, E., Wallace, M. B., & Repici, A. (2023). Practical integration of AI in the endoscopy suite: Recommendations for optimizing CADe and CADx tools. Clinical Gastroenterology and Hepatology, 21(4), 1031–1038. https://doi.org/10.1016/j.cgh.2022.07.025
7. Wang, P., Liu, X., Berzin, T. M., Glissen Brown, J. R., Liu, X., Zhou, C., … & Repici, A. (2022). Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe Study): A prospective, multicentre randomized trial. The Lancet Digital Health, 4(4), e215–e223. https://doi.org/10.1016/S2589-7500(21)00238-0