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EnterTheLoopentertheloop

Clinicians powering AI alignment, training & safety.

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© 2026 EnterTheLoop Ltd · Built in Britain

Enterprise Services

Clinical AI evaluation,
measured by people who practise medicine.

Calibrated UK clinicians score your medical AI — every metric backed by confidence intervals and full safety coverage, ending in evidence you can take to procurement and regulators.Calibrated UK clinicians score your medical AI — every metric backed by confidence intervals, inter-rater agreement, and full safety-taxonomy coverage. From structured evaluation and red-teaming to clinical datasets and a managed clinical workforce, every engagement ends in evidence you can take to procurement, regulators, and the lab — a Reliability Report, or training data with the rigour to back it.

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Clinical AI EvaluationItem 47 / 60

AI response · under review

For suspected anaphylaxis, give IM adrenaline 0.5 mg, repeated after 15 minutes if needed.

AEMPGPSPharmJOncLCardioTRenalHPaeds

Consensus score · n = 7

score95% CI
6080100799286

Mean

86

95% CI

79–92

Cohen's κ

0.84

Reliability Report generated

The data behind breakthroughs in Medical AI

Every leap in medical AI rests on judgement only practising clinicians can give.

Managed Clinical Workforce

You judge the work. We manage the people.

01

Register-checked clinicians

Checked against the GMC, NMC, GPhC or HCPC when the cohort is assembled, and re-checked quarterly.

02

You stay the acceptance authority

You review each new clinician's first outputs against your own rubric, and remain the final acceptance authority in every case.

03

One contract, one invoice

No engagement or payment ever runs between you and a clinician, and every output carries a clean chain of title.

Register-checked UK clinicians create clinical scenarios and clinician-authored reference responses inside your own environment — we source, contract, manage, replace and pay the people.

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Clinical Datasets

Expert labels you can build on.

01

Domain-expert labellers

Active GMC, NMC, GPhC, and HCPC registrants label your data — clinicians who live the context, not general annotators.

02

Quality-controlled

Gold-standard injection, drift detection, and attention checks hold consistency across the whole run.

03

Consensus + agreement

Multi-annotator consensus labels with full agreement statistics and per-annotator reliability scores.

Clinical NLP, medical chatbots, diagnostic models — get expert-labelled datasets with the audit trails to trust them for safety-critical work.

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Clinical AI Evaluation

Know exactly how much to trust your model.

01

Calibrated evaluators

UK-registered clinicians, calibrated before they score a single output.

02

Stats on every metric

Confidence intervals and inter-rater agreement — Cohen's κ and Fleiss' κ — reported on every result.

03

Full safety coverage

Severity-weighted accuracy across all 12 clinical failure-mode categories.

From triage to prescribing, we measure clinical accuracy, safety, and appropriateness — and tell you how reliable the verdict is.

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Clinical AI Red-Teaming

Find the failures before patients do.

01

Clinician adversaries

Trained clinicians probe your system the way a real GP, pharmacist, or ED doctor would — not generic prompt attacks.

02

12-category taxonomy

Structured adversarial testing across our clinician-built taxonomy of medical AI failure modes.

03

Severity-weighted report

Clinical-impact analysis, mitigation steps per failure mode, and a re-test protocol to validate fixes.

Dangerous dosing, false reassurance, contraindication misses, hallucinated diagnoses — we surface the safety-critical failures and show you how to close them.

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Healthcare AI Advisory

Clinical judgement on tap.

01

Practising specialists

Practising UK clinicians — current, real-world knowledge, not textbook theory.

02

Design to deployment

Product design, workflow integration, safety frameworks, and regulatory strategy at any stage.

03

Flexible engagement

From a one-off consultation to a standing clinical advisory board, structured around your team.

Direct access to the clinical expertise you need to build medical AI that works in real clinical settings — and stays safe once it gets there.

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The Deliverable

Every engagement ends in evidence you can defend.

Two procurement-grade deliverables — the same statistical rigour behind both.

Evaluation · Red-teaming

Reliability Report

A report on the system or its evaluators — the artifact you take to safety reviews, regulators, and procurement.

  • Per-metric confidence intervals
  • Coverage across all 12 safety categories
  • Full evaluator audit trail
Annotation · Datasets

Documented Dataset

The data itself — rights-clean and ready to train on, shipped with the provenance to defend it.

  • Calibrated clinician provenance
  • Inter-annotator agreement statistics
  • Full version history
  • Rights-clean, UK/EU-resident
See the methodologyTalk to our team
Why EnterTheLoop

What makes us different

Six reasons our clinical evaluation stands up to a regulator — not just another annotation vendor.

02.A

Clinical Experts

Every evaluator is a UK-registered healthcare professional — not a general annotator. They understand the clinical context because they work in it daily.

02.B

Statistical Rigour

Confidence intervals on every metric. Inter-annotator agreement. Proper scoring rules. You know exactly how much to trust the results.

02.C

Safety-First

Built on our 12-category clinical AI failure mode taxonomy. We test for the specific ways medical AI fails in practice.

02.D

Calibrated Evaluators

Every evaluator passes two-phase calibration before assessing your system. Their reliability is measured, not assumed.

02.E

Quality Control

Gold-standard injection, drift detection, attention checks, and justification analysis maintain quality throughout production.

02.F

Full Audit Trail

Complete records of every evaluation decision, justification, and quality metric.

02.A

Clinical Experts

Every evaluator is a UK-registered healthcare professional — not a general annotator. They understand the clinical context because they work in it daily.

02.B

Statistical Rigour

Confidence intervals on every metric. Inter-annotator agreement. Proper scoring rules. You know exactly how much to trust the results.

02.C

Safety-First

Built on our 12-category clinical AI failure mode taxonomy. We test for the specific ways medical AI fails in practice.

02.D

Calibrated Evaluators

Every evaluator passes two-phase calibration before assessing your system. Their reliability is measured, not assumed.

02.E

Quality Control

Gold-standard injection, drift detection, attention checks, and justification analysis maintain quality throughout production.

02.F

Full Audit Trail

Complete records of every evaluation decision, justification, and quality metric.

Scope a project

30-minute call. Get a quote.

Tell us what you’re building and what you need evaluated, red-teamed, annotated, or generated. We’ll come back with a scoped, priced brief — no long enterprise procurement.

  • A scoped, priced proposal
  • No drawn-out procurement process

NDA available on request.

EnterTheLoopentertheloopClinicians powering AI alignment, training & safety.

Verified against

GMCNMCGPhCHCPC

Follow

Register→
© 2026 EnterTheLoop Ltd  ·  Built in Britain
PrivacyTermsCookies
EnterTheLoopentertheloop

Clinicians powering AI alignment, training & safety.

PrivacyTermsCookies
© 2026 EnterTheLoop Ltd · Built in Britain