AI-Assisted Colonoscopy: Are We Really Missing Fewer Polyps?

Artificial intelligence (AI) has rapidly emerged as a transformative force in gastrointestinal endoscopy, particularly in the domain of colorectal cancer screening. Since the first randomized trials of computer-aided detection (CADe) systems began reporting favorable outcomes in 2019–2020, the past five years have witnessed a sharp acceleration in the development, regulatory approval, and clinical integration of AI technologies in colonoscopy. These tools are designed to enhance real-time polyp detection by highlighting suspicious lesions on-screen during withdrawal, thereby aiming to reduce the prevalence of missed adenomas and ultimately lower the rate of interval colorectal cancers — lesions that are present at the time of colonoscopy but go undetected and subsequently manifest as cancer within a surveillance window.

Early studies, often conducted in high-control research environments, demonstrated significant increases in adenoma detection rate (ADR), especially in diminutive lesions and flat right-sided polyps. This sparked a wave of optimism that AI could fill a critical quality gap in colonoscopy and offer consistent performance across varying operator skill levels and practice settings. Since then, several commercial platforms, including GI Genius, ENDO-AID, and Discovery, have received international clearance and are now used widely in both academic and community-based practices. However, as real-world data accumulates, the initial excitement has been tempered by more pragmatic questions. Are these improvements in ADR translating into clinically meaningful reductions in colorectal cancer incidence or mortality? Is the benefit consistent across patient subgroups and lesion types? To what extent do AI tools outperform high-definition white-light endoscopy (HD-WLE) when used by experienced endoscopists? And crucially, are the clinical gains worth the financial and workflow costs associated with these technologies?

This article explores the current evidence surrounding AI-assisted colonoscopy in 2025. Drawing from pooled analyses, prospective trials, and health-economic models, it evaluates the promise and limitations of CADe systems, their effect on interval cancer targets, their practical drawbacks, and their future trajectory in the era of foundation models and real-time histologic inference.

CADe vs. HD White-Light: Evolution of the Last Five Years

Over the past five years, computer-aided detection (CADe) has evolved from a promising adjunct to a widely deployed tool in colonoscopy suites around the world. Initially introduced to support detection among lower-performing endoscopists, CADe systems are now routinely paired with high-definition white-light endoscopy (HD-WLE) even in high-volume academic centers. The aim has been to enhance adenoma detection rate (ADR), reduce variability across operators, and improve the reliability of colorectal cancer screening.

Modern CADe platforms, including GI Genius, ENDO-AID, and Discovery, function as real-time overlays that highlight potential polyps during withdrawal. These systems use convolutional neural networks trained on large datasets of endoscopic video to flag suspected lesions, particularly diminutive adenomas, flat lesions, and right-sided polyps, which have historically been missed with higher frequency.

Multiple randomized controlled trials have validated these benefits. A prominent 2025 trial from Kuwait reported a 9.8% absolute increase in ADR in average-risk screening patients using CADe compared to HD-WLE alone, with no compromise in withdrawal time or procedure efficiency. These findings echo earlier multicenter trials that showed relative increases in detection, particularly in community settings or among endoscopists with lower baseline ADRs.

However, the benefit appears operator-dependent. In experienced hands, the marginal gain may be smaller, with studies showing ADR improvements of 2–4% in already high-performing practitioners. This has led to discussions around targeted deployment, using CADe selectively in training environments, for quality assurance, or in settings where ADRs consistently fall below benchmark thresholds.

As AI becomes increasingly embedded in routine practice, its role is shifting from a novelty to a standardized quality-enhancing tool, though not without trade-offs in clinical nuance and operator engagement.

2025 Pooled Analysis – Impact on Interval Cancer Targets

While early clinical trials of CADe systems emphasized improvements in adenoma detection rate (ADR), the more consequential question is whether this translates into fewer interval colorectal cancers (CRCs), those cancers that develop after a colonoscopy but before the next recommended surveillance. In 2025, a large pooled analysis of randomized controlled trials encompassing more than 14,000 patients provided the most comprehensive assessment to date of AI’s clinical utility beyond detection metrics (PMC11898786). The meta-analysis found that AI-assisted colonoscopy modestly reduced the interval cancer rate, with an estimated absolute risk reduction of 0.3% to 0.5% over three years. While statistically significant, the clinical impact remains modest. Importantly, the reduction in interval cancer was primarily driven by improvements in adenoma miss rate (AMR) and detection of flat or right-sided lesions, historically underdiagnosed in standard white-light endoscopy. However, there was no significant reduction in the detection of advanced adenomas or sessile serrated lesions, which are more strongly associated with malignant transformation.

The findings raise a critical question: is AI helping detect clinically meaningful lesions, or is it simply increasing the yield of diminutive polyps with limited neoplastic potential? This distinction matters, particularly as the incremental benefit must be weighed against procedural burden, patient anxiety, and cost-effectiveness. While CADe improves detection, its long-term impact on colorectal cancer outcomes remains an area requiring further prospective study, especially in diverse and real-world populations.

Limitations: False Positives and Algorithm Fatigue

Despite measurable gains in polyp detection, AI-assisted colonoscopy is not without drawbacks. One of the most frequently reported limitations of current CADe systems is the rate of false positives — alerts triggered by non-neoplastic visual artifacts such as mucosal folds, bubbles, residual debris, or even glare from lighting. These unnecessary prompts may distract endoscopists, slow withdrawal, and contribute to alert fatigue, especially during high-throughput clinical sessions.

Over time, frequent false alarms may shift the cognitive load of detection from human to machine, leading to over-reliance on the algorithm and reduced vigilance in visual inspection. Early observational data suggest that in some settings, particularly where CADe systems are used continuously throughout the day, endoscopists may become desensitized to alerts or begin to ignore them altogether—an effect analogous to the “alarm fatigue” documented in intensive care settings.

Another concern is the increase in low-value interventions, such as unnecessary polypectomies of non-neoplastic lesions, driven by AI’s heightened sensitivity. These procedures add cost, risk, and histopathologic workload without improving outcomes, and may alter follow-up intervals inappropriately under current surveillance guidelines.

There is also limited evidence on how AI performance is affected by procedural complexity, suboptimal prep, or non-standard patient anatomies, i.e., scenarios common in real-world practice. As with any clinical tool, AI must be viewed as an adjunct, not a substitute. Ensuring appropriate human–AI collaboration, with clear override protocols and ongoing training, is key to maximizing benefit while minimizing distraction and overreach.

Economics: Is the GI Genius Subscription Worth It?

As AI systems like GI Genius, Discovery, and ENDO-AID transition from clinical trial settings into routine practice, their financial sustainability has become a growing consideration for healthcare systems, hospitals, and independent endoscopy centers. Unlike traditional endoscopic hardware, CADe platforms typically require not only upfront equipment costs, but also ongoing subscription fees, cloud-based software updates, and technical support contracts. These costs may range from several thousand to tens of thousands of dollars annually, depending on system integration and licensing structure.

The key question is whether these expenses translate into measurable downstream savings or improved clinical outcomes that justify the investment. To date, no major health system in the United States or Europe offers separate reimbursement for AI-assisted colonoscopy, meaning providers must absorb the cost unless bundled within a broader quality-based incentive program.

Economic modeling studies have shown that CADe may be cost-effective in select scenarios, particularly when applied in low-ADR settings or when it enables extension of surveillance intervals through improved initial detection. However, these models often rely on idealized assumptions that may not reflect real-world practice variation. In high-volume centers with experienced endoscopists and already-high detection rates, the marginal gain offered by CADe may not offset its recurring cost. Furthermore, there is limited data on return on investment (ROI) in terms of reduced litigation, improved patient satisfaction, or long-term cancer prevention — key metrics for payers and administrators. Some institutions may find the value of AI lies more in training, quality benchmarking, and performance auditing than in direct diagnostic yield.

Ultimately, the cost-benefit profile of CADe systems remains institution-specific. For some, the technology represents a strategic investment in quality assurance; for others, it may remain a promising but financially unviable enhancement, at least under current reimbursement structures.

Future: Foundation Models and Zero-Shot Segmentation

The future of AI in colonoscopy is poised to move beyond narrow, task-specific algorithms toward foundation models: large-scale, pre-trained architectures capable of generalized learning, contextual interpretation, and zero-shot segmentation. Unlike traditional CADe systems, which rely on rigid datasets annotated for polyp detection alone, foundation models are designed to extract patterns across a wide range of visual features and clinical tasks without requiring retraining for each application.

A recent 2025 study published on arXiv demonstrated the feasibility of deploying foundation models for zero-shot polyp segmentation, with strong cross-device and cross-center generalizability. These models successfully interpreted endoscopic frames from different manufacturers and patient populations, suggesting that such systems could adapt to real-world variation better than conventional CADe tools. Additionally, they showed early promise in lesion characterization, mucosal inflammation scoring, and automated report generation.

Beyond colonoscopy, foundation models have potential applications in upper GI endoscopy, capsule endoscopy, and even non-invasive imaging modalities. By integrating textual prompts and clinical context, these systems may eventually support real-time histologic inference, predict the likelihood of dysplasia, or triage findings according to urgency. However, several challenges remain before widespread deployment. These models require significant computational infrastructure, raise important questions about data governance and privacy, and need prospective clinical validation across institutions and populations. There are also concerns about interpretability and regulatory approval pathways.

Despite these hurdles, foundation models represent a paradigm shift in AI-assisted endoscopy, from binary detection to cognitive augmentation, where machines don’t just highlight abnormalities, but help interpret them in real time, adapting dynamically to context, operator skill, and patient-specific features.