The role of AI in improving or impairing colonoscopy adenoma detection

In August 2025, The Lancet Gastroenterology & Hepatology published a finding that travelled the world: at four Polish centres, endoscopists who had spent months working with AI-assisted detection found fewer adenomas when performing a colonoscopy without it. The rate fell from 28.4% to 22.4%. Four months later, a Norwegian prospective trial with 5,013 colonoscopies and three phases found exactly nothing: neither loss nor gain of skill when the tool was withdrawn. And a third study, over three years, found sustained improvement. This article sorts out a scientific controversy that is live right now, and explains what separates the three designs.

The 30-second summary: the seven figures that matter

Each row names the study and the year. Note that the first three contradict one another and that all three are well done.

What you will find here

• The Polish study that raised the alarm and how it is built.

• The three-phase prospective trial that did not find the effect.

• What has been measured outside endoscopy: radiology and pathology.

• The uncomfortable detail about which lesions are detected more and less.

The study that raised the alarm

Krzysztof Budzyń, Marcin Romańczyk alongside a broad team released a retrospective observational study in The Lancet Gastroenterology & Hepatology during 2025, which took place across four Polish endoscopy centres involved in the ACCEPT trial. Polyp detection tools were adopted by those facilities at the close of 2021, prompting the researchers to evaluate colonoscopy standards delivered without AI throughout the preceding three months against the subsequent three months following that deployment.

The sample comprised 1,443 patients, encompassing 795 prior to and 648 following the intervention, alongside a median age of 61 years. The main endpoint evaluated was the adenoma detection rate achieved during conventional, unassisted colonoscopy examinations.

The result: detection fell from 28.4% (226 of 795) to 22.4% (145 of 648), an absolute difference of −6.0 percentage points (95% CI: −10.5 to −1.6; p = 0.0089). In multivariable analysis, prior AI exposure remained an independent factor associated with that fall, with an odds ratio of 0.69.

What that design cannot separate

It is a retrospective observational study comparing two three-month periods. Anything that changed at those centres between late 2021 and early 2022 — staffing, workload, the kind of patient referred — enters the comparison alongside the AI. The authors phrase it cautiously: continuous exposure “might” reduce detection, “suggesting” a negative effect.

The trial that did not find the effect

Tor Anders Pedersen, Yuichi Mori along with their colleagues released a pragmatic, prospective, multicentre study in Endoscopy during late 2025 that was specifically formulated to resolve this inquiry. A total of 5,013 colonoscopies were performed across three sequential stages—pre-AI, during AI deployment, and post-withdrawal—by thirteen endoscopists, comprising seven novices and six veterans who had each completed upwards of a thousand procedures. Consequently, every clinician served as their own control.

The authors’ conclusion is clear: detection by inexperienced endoscopists increased while they were using the tool, and in unassisted colonoscopy neither improvement nor loss of skill was observed after the exposure period.

This layout surpasses the Polish alternative for this particular inquiry, owing to its prospective nature and the fact that every practitioner serves as their own benchmark. Furthermore, it involves a smaller cohort—thirteen compared to the participants across four facilities—and relies on an indirect metric, namely the fraction of colonoscopies revealing at least one polyp measuring 5 mm or greater, which lacks the established standing as a quality benchmark that adenoma detection enjoys.

What has been assessed beyond endoscopy

Pierre Heudel and his research team shared a scoping review addressing clinical deskilling in ESMO Real World Data and Digital Oncology throughout 2026. Their primary takeaway: data regarding professional deskilling remains limited yet uniform across medical fields. Three distinct metrics highlighted by the authors:

• In radiology, a controlled study with 27 breast imaging radiologists found that erroneous AI prompts increased false-positive recalls by up to 12%, even among experienced readers.

• In computational pathology, in experimental tasks under time pressure, more than 30% of participants reversed a correct initial diagnosis when exposed to an incorrect system suggestion.

• In cytology, the United Kingdom’s shift to primary human papillomavirus screening cut case volumes by 80% to 85% and consolidated laboratories from 45 to 8 centres, with direct consequences for training capacity.

That last point introduces a distinction worth keeping. A professional losing their edge by leaning on the machine is not the same as a system that stops generating the cases people learn on. The second problem is structural and cannot be fixed with individual training.

The uncomfortable detail: what is detected more and less

An observational investigation spanning three years in Japan, encompassing upwards of 4,700 paired colonoscopies, revealed zero decline following the adoption of assisted detection, while additionally demonstrating expedited learning curves for endoscopists who already possessed strong detection capabilities. This appeared to decisively resolve the controversy.

Darrien Henry and James East published a letter in Endoscopy in 2026 reading those same data another way. In that study, detection rates for adenomas of 6 to 9 mm, 10 mm or larger, with high grade dysplasia, and for invasive cancer were significantly higher in the group without assistance. The invasive cancer rate was more than double without the tool than with it (p = 0.001).

Their alternative interpretation is harsh and well argued: assisted detection may distract the endoscopist towards trivial lesions — adenomas of 1 to 5 mm and hyperplastic polyps — at the expense of advanced lesions, which are the ones that matter. The consistency of the effect direction across three different categories of advanced lesion, with very low p values, makes chance an unlikely explanation.

Omer Ahmad proposed in 2026, also in Endoscopy, the reading that best reconciles the three studies: they may not contradict one another, but rather be capturing different stages of human-machine adaptation, and what the Polish study measured may be transient, reversible cognitive offloading rather than permanent erosion of skill.

What to do with this, in practice

Ordered by the strength of the evidence behind it:

• Do not treat the debate as settled in either direction. There are three serious studies with three different results, and the difference is in the design.

• Keep periods of unassisted practice. It is the recommendation that recurs in the endoscopy literature, precisely because of what is not known.

• Measure what kind of lesion you detect, rather than simply counting them. The specialty’s most rigorous review revealed that a higher volume of invasive cancer was uncovered without the software than with its assistance.

• Watch for erroneous prompts. In breast radiology they increased false-positive recalls by up to 12%, including among experienced readers.

• Think about training, not only about the already-trained professional. The British cytology case shows the problem may be that the cases people learn on disappear.

Frequently asked questions

Do doctors who use artificial intelligence lose skill?

It remains unconfirmed. A Polish observational study featured in The Lancet Gastroenterology & Hepatology during 2025 revealed that unassisted adenoma detection rates dropped from 28.4% to 22.4% following the integration of AI, representing a 6.0-point decrease (p = 0.0089), whereas a subsequent prospective trial encompassing 5,013 colonoscopies detected neither decline nor improvement after the technology was removed.

Why do two good studies reach opposite conclusions?

Because of the design. The Polish trial is retrospective and evaluates a pair of three-month intervals, meaning that any shift at the core impacts the analysis; conversely, the Norwegian investigation is prospective, structured in three phases, and positions each endoscopist as their own baseline. A theory featured in Endoscopy suggests that both approaches could be documenting distinct phases of adaptation to the device.

Who does it impact the most?

The data points toward varying impacts depending on expertise. During the prospective study, novices boosted their detection rates with the assistance of the AI, climbing from 31.9% to 39.5% (OR 1.43), and dropped back to their initial levels once it was removed; conversely, among seasoned professionals, no meaningful difference was observed across any of the three stages.

Does it occur outside the endoscopy setting as well?

There are signals in other specialties. Based on the 2026 review, a controlled trial involving 27 breast imaging radiologists revealed that flawed AI prompts elevated false-positive recalls by as much as 12% even among seasoned specialists, whereas in computational pathology over 30% of participants flipped a correct diagnosis when confronted with a faulty suggestion amid time constraints.

Does finding a greater number of polyps translate to stopping more cancer?

Not necessarily, and that is the most uncomfortable point. Revisiting the data from a three-year study, a letter published in Endoscopy in 2026 revealed that the detection of adenomas measuring 10 mm or greater, cases showing high-grade dysplasia, and instances of invasive cancer proved notably greater in the absence of the device, yielding an invasive cancer rate that exceeded double the amount (p = 0.001).

What we do not know

• Whether the effect is reversible. The transient cognitive offloading hypothesis remains plausible, yet nobody has measured what happens after withdrawing the tool for an extended period.

• What happens to individuals instructed with the software right from the beginning. Existing research fails to cover complete novices, precisely the demographic where long-term skill retention is most critical.

• Whether it affects mortality. None of this work measures post-colonoscopy colorectal cancer or survival.

• How long the exposure needs to be. One study, which observes an impact, spans three months; another, showing no effect, evaluates varying durations; finally, the research noting enhancement lasts three years.

References

1. Budzyń, K., Romańczyk, M. et al. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. The Lancet Gastroenterology & Hepatology, 2025. DOI: 10.1016/s2468-1253(25)00133-5

2. Pedersen, T. A., Mori, Y. et al. Learning and deskilling effects of artificial intelligence in colonoscopy among endoscopists with different levels of experience: a pragmatic, prospective trial. Endoscopy, 2025. DOI: 10.1055/a-2858-7084

3. Heudel, P., Crochet, H. et al. Artificial intelligence in medicine: a scoping review of the risk of deskilling and loss of expertise among physicians. ESMO Real World Data and Digital Oncology, 2026. DOI: 10.1016/j.esmorw.2026.100693

4. Henry, D. and East, J. Artificial intelligence-supported polyp detection (CADe) at colonoscopy reduces the detection of high grade dysplasia and invasive cancer. Endoscopy, 2026. DOI: 10.1055/a-2749-8345

5. Ahmad, O. Human-artificial intelligence adaptation in colonoscopy: beyond deskilling and upskilling. Endoscopy, 2026. DOI: 10.1055/a-2896-9791

This content is for information only and does not constitute medical advice. The figures cited come from studies with designs, specialties and durations that are not comparable with one another, and describe a scientific debate that was open at the time of writing.