Sustained improvement in adenoma detection with prolonged AI use

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trial with 5,013 colonoscopies and three phases found exactly nothing: neither loss nor gain of skill when the tool was withdrawn.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 regarding which lesions are identified with greater and lesser frequency.

The study that raised the alarm

Krzysztof Budzyń, Marcin Romańczyk and a large team published in The Lancet Gastroenterology & Hepatology in 2025 a retrospective observational study at four Polish endoscopy centres taking part in the ACCEPT trial. Those centres introduced polyp detection tools at the end of 2021, and the authors compared the quality of colonoscopies performed without AI in the three months before and the three months after that rollout.

The sample: 1,443 patients, 795 before and 648 after, with a median age of 61. The primary outcome was the adenoma detection rate in standard, unassisted colonoscopy.

The outcome: detection dropped from 28.4% (226 out of 795) down to 22.4% (145 out of 648), representing an absolute variance of −6.0 percentage points (95% CI: −10.5 to −1.6; p = 0.0089). According to multivariable assessment, past AI familiarity stayed an independent variable linked to this decline, yielding 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.

It is a better design than the Polish one for this specific question, because it is prospective and each professional is their own control. It also has fewer professionals, thirteen against those at four centres, and uses an indirect measure — the proportion of colonoscopies with at least one polyp 5 mm or larger — which is not as established a quality indicator as adenoma detection.

What has been measured outside endoscopy

Pierre Heudel and colleagues published a scoping review on deskilling in medicine in ESMO Real World Data and Digital Oncology in 2026. Their general conclusion: evidence of clinical deskilling is scarce, but consistent across specialties. Three specific figures they record:

• In radiology, a controlled study involving 27 breast imaging radiologists revealed that flawed AI prompts elevated false-positive recalls by as much as 12%, even among seasoned professionals.

• 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 final detail highlights a distinction worth remembering. When an expert misses their touch through overreliance on automation, it differs fundamentally from an ecosystem failing to produce the foundational scenarios trainees study. The latter issue runs deep structurally and defies resolution via personal coaching.

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 suggested in 2026, also within Endoscopy, the interpretation that most effectively harmonizes the three investigations: these trials might not be contradictory, but instead capture various phases of human-machine adaptation, meaning that what the Polish research evaluated could represent temporary, reversible cognitive offloading instead of lasting skill degradation.

Practical application of these steps

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. Within breast radiology, they elevated false-positive recalls by as much as 12%, even among seasoned practitioners.

• 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 is not settled. The Polish observational study published in The Lancet Gastroenterology & Hepatology in 2025 found unassisted adenoma detection falling from 28.4% to 22.4% after AI was introduced, a difference of −6.0 points (p = 0.0089), but a later prospective trial with 5,013 colonoscopies observed neither loss nor gain after the tool was withdrawn.

Why do two sound studies arrive at conflicting 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 evidence points to different effects by level. In the prospective trial, inexperienced endoscopists improved detection while using the AI, from 31.9% to 39.5% (OR 1.43), and returned to their baseline when it was withdrawn; among the experienced there was no significant effect in any of the three phases.

Does it occur outside the endoscopy setting as well?

There are signals in other specialties. According to the 2026 review, a controlled study with 27 breast imaging radiologists found erroneous AI prompts increased false-positive recalls by up to 12% even among experienced readers, and in computational pathology more than 30% of participants reversed a correct diagnosis when faced with an incorrect suggestion under time pressure.

Does detecting more polyps mean preventing more cancer?

Not necessarily, and that is the most uncomfortable point. Reanalysing the data from a three-year study, a letter published in Endoscopy in 2026 showed that detection of adenomas 10 mm or larger, of high grade dysplasia and of invasive cancer was significantly higher without the tool, with an invasive cancer rate more than double (p = 0.001).

What we do not know

• Whether the effect is reversible. The transient cognitive offloading hypothesis is plausible, but nobody has measured what happens after withdrawing the tool for a long period.

• What happens to people trained with the tool from the start. The available studies do not include absolute beginners, the group where skill retention matters most.

• 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.