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RESEARCH PREVIEW · evidence cutoff 2026-08-27
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Research preview · Vendor evidence as of 2026-08-27. Labels describe that snapshot, not a live assessment. Coverage & limitations

Start here · 5 min read

Evaluate healthcare AI before you buy.

A practical sequence for turning a promising demo into a question you can actually test.

The useful distinction

Start with a workflow and a measurable outcome. A product demonstration is the beginning of the evaluation, not its result.

01

Define the decision before the product

Write a one-sentence use case: who will use the system, for which task, in which setting, and with what human review. Keep the proposed change small enough to evaluate. A tool that drafts a note and a tool that recommends treatment need different scrutiny.

Questions to take into the conversation

  • What is the current process, including its failure modes?
  • Which outcome would justify a change, and who owns it?
  • What tasks must remain outside the system's scope?
02

Ask for evidence that matches your setting

Separate a claim's source, the quality of its study, and its relevance to your population. Finding a sentence in a vendor announcement verifies what was said; it does not independently establish the outcome. A published average can also conceal poor performance in a subgroup.

Questions to take into the conversation

  • Was this product version evaluated with comparable users and patients?
  • What was the comparator, sample size, study period, and missing-data rate?
  • Who funded the work, and can you inspect the methods and limitations?
03

Design the evaluation and the exit together

Our recommended evaluation sequence is: document the baseline, test approved examples, run a bounded pilot, and review the result against criteria agreed in advance. Name a decision owner and a stop condition. NIST's voluntary AI RMF offers a broader structure through Govern, Map, Measure, and Manage.

Questions to take into the conversation

  • Who reviews errors and can pause use?
  • What changes after a model or workflow update?
  • Can you export your data and return to the prior workflow?

Sources & scope

This guide combines HealthIT's suggested evaluation questions with the primary references below. It is educational material; application depends on the specific product, workflow, organization, and jurisdiction.

Found something that needs correction? Send a source-backed correction.