AGA’s Cautious Stance on AI-Assisted Colonoscopies

On March 20, 2025, the American gastroenterological Association (AGA) issued a new clinical guideline regarding the use of computer-aided detection (CADe) systems during colonoscopies. While acknowledging the technology’s ability to enhance polyp detection, the AGA stopped short of recommending worldwide adoption, citing a lack of conclusive evidence linking AI assistance to reduced rates of colorectal cancer.

Colorectal cancer remains a meaningful health concern in the United States.Colonoscopy is performed over 15 million times each year in the U.S., and remains a vital tool in both detecting and preventing the disease. CADe systems have demonstrated the ability to improve polyp detection during these procedures, however, the question of weather this leads to fewer cases of colorectal cancer remains unanswered.

The AGA’s guideline reflects a nuanced understanding of the current state of AI in gastroenterology. While acknowledging the potential benefits, it stresses the importance of rigorous validation and a focus on patient outcomes.

Expert Perspectives: Enthusiasm Tempered by the Need for more Data

Leading gastroenterologists recognize the promise of AI in colonoscopies, but emphasize the need for further research before widespread implementation.

According to one of the guideline authors, Benjamin Lebwohl, MD, AGAF, “We are confident that using AI will led to more polyps removed and more colonoscopies. We’re less sure about the extent to which it will lead to less colon cancer. AI-assisted colonoscopy technology is promising and exciting. Its reasonable for practitioners to use the tech now, but we’re not yet at a point where we can recommend universal adoption.”

This sentiment is echoed by other experts in the field, who emphasize the importance of focusing on patient outcomes rather than simply increasing polyp detection rates.

If AI is going to be impactful,it needs to be better than the human eye. Right now, AI is detecting easy-to-detect lesions.This is version 1.0. before we can recommend everyone use AI, we need version 4.0, where it helps detect polyps that are truly tough to find.”

Shahnaz Sultan, MD, MHSc, AGAF, guideline author

Key Knowledge Gaps and Future Research Directions

The AGA guideline identifies specific areas where further research is needed to fully assess the value of AI-assisted colonoscopies. The AGA plans to update the guideline in one to two years as more data linking the use of CADe in colonoscopy to improve patient outcomes becomes available.

• Quality over quantity: The focus shoudl be on patient outcomes, such as post-colonoscopy colorectal cancer rates, rather than just polyp detection.
• Transparency in AI research: More publicly available data is needed to ensure AI models are rigorously compared and improved.

these recommendations highlight the importance of moving beyond simply detecting more polyps and focusing on whether AI can ultimately reduce the incidence of colorectal cancer and improve patient survival rates. This includes understanding the potential for AI to identify subtle or difficult-to-detect lesions that might be missed by human observers.

The Cost-Benefit Analysis and Practical applications in the U.S. Healthcare System

The implementation of AI in colonoscopies also raises crucial questions about cost-effectiveness and resource allocation within the U.S. healthcare system. While AI-assisted systems may improve detection rates, the initial investment in technology and training coudl be substantial. A thorough cost-benefit analysis will be crucial to determine whether the widespread adoption of AI is economically sustainable.

Such as, a community hospital in rural Kansas might face different challenges and opportunities compared to a large academic medical center in New York City. Factors such as patient demographics,access to specialists,and available resources will all play a role in determining the optimal approach to AI implementation.

Addressing Potential counterarguments and concerns

Despite the potential benefits, it’s critically important to acknowledge potential counterarguments and concerns surrounding the use of AI in colonoscopies. One concern is the potential for over-reliance on AI, which could lead to a decline in the skills and expertise of human endoscopists.

Another concern is the potential for bias in AI algorithms. If the data used to train AI models is not representative of the broader population, the system may perform less accurately for certain demographic groups, leading to disparities in care. For example, if the training data primarily includes images from Caucasian patients, the AI system may be less effective at detecting polyps in patients of other ethnicities.

Understanding Colorectal Cancer: A Persistent Threat

Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States.Regular screening, including colonoscopies, is crucial for early detection and prevention. The AGA’s guideline underscores the importance of continued research and innovation to improve the effectiveness of these screening efforts.