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Guide 06 — AI Evaluation in Healthcare

From model metrics to safety, workflow value and real-world performance.

Four distinct evaluation families, the lifecycle from offline test set to production monitoring, and the pitfalls in between.

A4 sheet — scroll sideways or pinch to zoom

Visual Guide 06 · Evaluation / Foundation / Builder

AI Evaluation in Healthcare

From model metrics to safety, workflow value and real-world performance.

Healthcare AI Learning

A

Predictive / classification models

  • Sensitivity · specificity
  • PPV / NPV (prevalence-dependent)
  • AUROC / AUPRC where appropriate
  • Calibration
  • Threshold-dependent errors / confusion matrix

Choose metrics from the intended use and the consequences of false positives and false negatives.

B

Generative AI

  • Factuality / correctness
  • Grounding to supplied sources
  • Relevance · completeness
  • Robustness to phrasing and context
  • Consistency where it matters
  • Harmful or unsafe output rate

Grounding does not automatically equal truth: retrieval can be wrong or incomplete.

C

Tools / agents / workflows

  • Task success
  • Tool-call correctness
  • Permission adherence
  • Step completion
  • Invalid or harmful action rate
  • Escalation behaviour
  • Latency · cost / resource use

A fluent final answer can hide a failed process or tool call.

D

Real-world clinical & operational value

  • Adoption and use
  • Time saved · error / rework rate
  • Safety incidents
  • Workflow burden
  • Clinician and patient experience
  • Subgroup performance / equity
  • Patient or clinical outcomes where relevant
  • Financial and operational impact

Good model performance ≠ clinical value.

Evaluation lifecycle

Offline test set

Silent / shadow evaluation where feasible

Controlled pilot

Production monitoring

Incident review, revalidation, change control

Pitfalls

  • One aggregate accuracy number
  • AUROC without the operating threshold or use case
  • Judging generative output only by “looks good”
  • No subgroup or error analysis
  • No workflow or human-factors evaluation
  • Launch without monitoring for drift and change

Evaluation is a system: technical quality + safety + people + workflow + impact.

Healthcare AI Learning · Visual Guide 06Updated Sep 2026

Summarises the sources cited in the related Healthcare AI Learning courses: AI in Healthcare, Building GenAI Applications in Healthcare, Agentic AI in Healthcare. Updated Sep 2026; regulatory content checked between 25 August and 10 September 2026. Educational summary only — not legal or clinical advice.

Healthcare AI Learning · Visual Guide 06: AI Evaluation in Healthcare

Updated Sep 2026 · Guides summarise the same primary sources cited in the related courses — official EU legal texts, standards bodies and peer-reviewed literature. Educational summaries only, not legal or clinical advice; regulatory dates were checked between 25 August and 10 September 2026.

Related learning

AI in HealthcareOpen
Building GenAI Applications in HealthcareOpen
Agentic AI in HealthcareOpen

Guides summarise the same primary sources cited in the related courses — official EU legal texts, standards bodies and peer-reviewed literature. Educational summaries only, not legal or clinical advice; regulatory dates were checked between 25 August and 10 September 2026.

Updated Sep 2026