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Health & Life SciencesResearch paperSource analysis

Can AI explain bowel leakage safely? A 20-answer study found weak next-step guidance

Three clinicians rated answers from four chatbots to five commonly searched phrases. The median actionability score was 40%, and every model produced inaccurate or irrelevant statements.

By The Impact of AI Editorial DeskReleased 26 October 2026 at 14:58 GMT3 min full report · 1 min summary1 source · view evidence
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The short version

What happened
Researchers tested five common US search phrases once in each of four free chatbots, producing 20 answers that three clinicians scored with DISCERN and PEMAT.
Who it affects
People who hesitate to discuss bowel leakage may turn to a chatbot first, but high reading levels and weak action steps can leave them unsure what to do or why professional assessment matters.
What remains unproven
Only five prompts and 20 one-shot answers

Keep in mind

Only five prompts and 20 one-shot answers

Short Google search phrases may not resemble conversational patient questions

Outputs were generated in February 2025 from model versions now replaced

Poor rater agreement for the understandability measure

No patient comprehension, behaviour, diagnosis or health outcome was measured

Source analysis · 1 linked source. Primary analysed source date: 26 October 2026.

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Read the full report · 3 min The evidence, methods, implications and limitations

Clear-looking answers often lacked usable next steps

A peer-reviewed US study found that chatbot answers about fecal incontinence could look organised while still giving patients weak practical guidance. Researchers took five commonly searched phrases from Google Trends and entered each once into ChatGPT 4o mini, Perplexity sonar, Llama 3.2 and Gemini 2.0 flash on 24 February 2025, producing 20 answers.

Three pelvic-health clinicians rated the answers. Median quality was 44 out of 80 on DISCERN, while median PEMAT actionability was 40%. Readability ranged from a median US grade level of 9.4 for Perplexity to 13.0 for Llama—above the fifth- to sixth-grade target cited for consumer information. A colorectal surgeon separately recorded 25 inaccurate or irrelevant statements: three for Llama, four for Gemini, eight for ChatGPT and ten for Perplexity.[1]

A small baseline, not a ranking of today's chatbots

The apparent 77.8% understandability score needs care: agreement between raters was poor, so the authors said understandable content could not be concluded confidently. Model-level quality and readability differed overall, but the study had only five paired prompts; no pairwise comparison remained significant after correction. It cannot support a league table.

The queries were brief search terms rather than detailed patient questions, each answer was sampled once, and all four model versions are now superseded. No patients tested comprehension, followed advice or reported outcomes. For someone seeking help with an embarrassing condition, the useful message is practical: chatbot text may be a starting point for questions, not a substitute for clinical assessment or an authoritative action plan. Repeated testing of current models with patient-written prompts and measured comprehension would change the assessment.[1]

The distinction that matters

What the 20-answer evaluation found

Five short search phrases were entered once into four free model versions; clinicians scored the resulting text rather than testing patients.

Quality

Three raters used the 16-item DISCERN tool

Median 44/80

Actionability

Raters applied the PEMAT next-step criteria

Median 40%

Clinical review

A surgeon checked relevance and accuracy

25 inadequacies across 20 answers

The small, one-shot evaluation describes February 2025 outputs; it is not a test of current systems or patient benefit. Source: Techniques in Coloproctology
National Cancer Institute researcher Chanelle Case Borden preparing genetic samples for PCR.

Illustrative photo · not a video frame

The Royal Society · 44 min 28 sec · Background explainer

Is this the AI future we want? | The Royal Society

YouTube loads only when you choose; playback starts when you press play.

A Royal Society discussion with Madhumita Murgia and biomedical engineer Alison Noble on AI’s potential in healthcare, science and culture, recorded at the 30 June 2026 event.

Evidence check: A discussion of opportunities and risks, including future possibilities. It is not evidence that every application described is available or clinically proven.

Published 2026-07-01. Source checked 14 October 2026. Check captions on the publisher’s video page.

Check the healthcare evidence

Photo: Daniel Sone / National Cancer Institute / Unsplash · Licence

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Read next

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What this means for people

  • People who hesitate to discuss bowel leakage may turn to a chatbot first, but high reading levels and weak action steps can leave them unsure what to do or why professional assessment matters.

Global context

The prompts came from US Google Trends and the clinical raters worked in New York. Terminology, health literacy, access to care and model localization differ internationally, so the measured scores should not be assumed to transfer unchanged.

What the evidence does not yet show

  • Only five prompts and 20 one-shot answers
  • Short Google search phrases may not resemble conversational patient questions
  • Outputs were generated in February 2025 from model versions now replaced
  • Poor rater agreement for the understandability measure
  • No patient comprehension, behaviour, diagnosis or health outcome was measured

What to watch next

  • Repeated sampling from current models with version and date recorded
  • Patient-authored prompts and observed comprehension across literacy levels
  • Tests against current clinical guidance in multiple countries
  • Whether citations, safety advice and concrete next steps improve without adding complexity

Living evidence record

Impact record IAI-0WHAGYX

Explore the full tracker

Evidence stage

Studied

Confidence

Supported

Reporting basis

Source analysis

Independent or research support

Present

Record status

Monitoring

Last checked

26 October 2026

Source trail

1 direct source across 1 source type.

People impact

Documented in this record.

Uncertainty

Limits and next checks are explicit.

Stages describe the evidence available—not whether a technology is good or bad. See the public method.

Single-source reporting disclosure

This record analyses one direct source. It can establish what Techniques in Coloproctology published or reported, but it is not independent corroboration of every performance claim or predicted outcome. The confidence label will change only when broader evidence is added.

Evidence trail

Sources used for this report

Links checked 26 October 2026

Research question, topics and regions

The quality, understandability, actionability and readability of chatbot answers to common US searches about fecal incontinence

Topics: Patient information · Health literacy · Large language models · Pelvic health

Regions: North America · Global

This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.

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