Comparing Polish and Norwegian trials on AI-assisted colonoscopy skills

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trial with 5,013 colonoscopies and three phases found exactly nothing: neither loss nor gain of skill when the tool was withdrawn.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: 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

This retrospective observational study compares two distinct three-month intervals. Anything that shifted across these sites between late 2021 and early 2022 — staffing levels, task volume, or patient demographics — enters the evaluation alongside the AI tool. The researchers express this cautiously, noting that continuous exposure “might” diminish detection rates, thereby “suggesting” a detrimental impact.

The trial that did not find the effect

Tor Anders Pedersen, Yuichi Mori and colleagues published in Endoscopy in late 2025 a pragmatic, prospective, multicentre trial designed specifically to answer this question. Thirteen endoscopists — seven inexperienced and six experienced, defined as having performed more than a thousand colonoscopies — carried out 5,013 colonoscopies across three successive phases: before AI, with AI, and after it was withdrawn. Each professional therefore acted 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 assessed beyond 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, during time-sensitive experimental trials, upwards of 30% of participants overturned a right initial diagnosis upon receiving a flawed system recommendation.

• 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 interpreting those identical statistics through a different lens. Within that research, detection rates for adenomas measuring 6 to 9 mm, 10 mm and above, those featuring high grade dysplasia, along with invasive cancer cases, proved markedly superior among the unassisted cohort. Furthermore, the incidence of invasive cancer was more than twice as high when the tool was absent compared to when it was utilized (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 view the discussion as completely resolved on either side. Three rigorous analyses point to three distinct outcomes, and their divergent methodologies account for the variance.

• 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, not just how many. The field’s sharpest analysis found more invasive cancer detected without the tool than with it.

• 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 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 one is retrospective and compares two three-month periods, so any change at the centre enters the comparison; the Norwegian one is prospective, three-phase, and each endoscopist acts as their own control. A hypothesis published in Endoscopy holds that both may be capturing different stages of adaptation to the tool.

Who does it affect 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 happen outside endoscopy too?

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 finding a greater number of polyps translate to stopping 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 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.