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AI Hallucinations in Healthcare: A Practical Risk Checklist (UK-First, Non-Clinical)

A real-world checklist for clinicians and teams: how hallucinations happen, how to pressure-test tools, and what ‘safe behaviour’ looks like day-to-day.

Review date
Jan 2026
Reading time
11 min read
Author
Dr Kola Tytler MBBS CertHE MBA MSt MRCGP
On this page
Career toolkitPractical prompts and templates for applications, portfolios and professional development. Check local employer and regulator requirements before use.

Key points

  • Hallucinations are best managed as a systems risk: define what ‘major’ vs ‘minor’ means, test it, then train safe behaviour.
  • Pressure-test any AI tool with adversarial prompts, boundary conditions, and ‘I don’t know’ expectations.
  • A credible evaluation should be explicit about error definitions and human review—don’t accept vague reassurance.
Healthcare isn’t the place for ‘close enough’. Hallucinations matter because they can create false confidence. The most robust approach is not fear; it’s discipline: define error severity, run structured tests, and train clinicians to verify high-impact claims. Recent UK-facing evaluation work has explicitly simulated hallucination behaviours and categorised ‘major’ hallucinations in clinical contexts—use that mindset even when you’re evaluating commercial tools.

Pressure-test checklist (use on any AI clinical search tool)

1

1) Boundary prompts

Ask the tool questions slightly outside its intended scope and observe behaviour. A safe tool should either refuse or clearly state uncertainty—not invent confident detail.
2

2) Citation stress test

Ask for the source behind the strongest claim and open it. If sources are not accessible or don’t support the claim, downgrade trust immediately.
3

3) Consistency test

Re-ask the same question with minor phrasing changes. Large swings in answers without explanation are a risk signal.
4

4) ‘I don’t know’ expectations

Tools that claim ‘no hallucinations’ or similar should explicitly disclose what happens when evidence is absent. A good system will stop rather than guess.
5

5) Human factors

Train a habit: if the output would materially change an action, verify the primary source. This is safety behaviour, not mistrust.

Use UK-style evaluation framing

Prefer evaluation language that defines ‘major’ vs ‘minor’ hallucinations and uses structured review, rather than marketing reassurance. This is how you make safety operational.
SourceExample: UK hallucination simulation framing (MHRA report PDF)
Open Link
SourcePraktiki: ‘no hallucinations’ claim (official)
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  • Ask the tool questions slightly outside its intended scope and observe behaviour. A safe tool should either refuse or clearly state uncertainty—not invent confident detail.

    Return to this section

  • Ask for the source behind the strongest claim and open it. If sources are not accessible or don’t support the claim, downgrade trust immediately.

    Return to this section

  • Re-ask the same question with minor phrasing changes. Large swings in answers without explanation are a risk signal.

    Return to this section

  • Tools that claim ‘no hallucinations’ or similar should explicitly disclose what happens when evidence is absent. A good system will stop rather than guess.

    Return to this section

  • Train a habit: if the output would materially change an action, verify the primary source. This is safety behaviour, not mistrust.

    Return to this section

Portfolios and governance · Article 6 of 12