Health organizations should evaluate conversational AI across seven connected domains: intended use, user needs, output quality, safety, bias and equity, privacy and governance, implementation, and ongoing monitoring. Testing should progress in stages, with stronger evidence required as the tool’s autonomy and potential consequences increase.
1. What is the intended use?
Begin with a precise statement of who the system is for, what it is intended to do, where it will be used, and what it must not do. A tool that summarizes public information has a different risk profile from one that screens symptoms, recommends services, or responds to distress.
2. What evidence supports the underlying need?
Evaluation should establish that the proposed system addresses a real problem for users and organizations. Co-design and formative research can help distinguish genuine needs from assumptions made by developers or purchasers.
3. How good are the outputs?
Accuracy alone is not enough. Reviewers may need to assess appropriateness, completeness, uncertainty, tone, recovery orientation, cultural context, actionability, and whether the response asks for enough information before reaching a conclusion.
4. What happens when the system is wrong?
Safety testing should identify foreseeable failures, their severity, how easily users can recognize them, and what safeguards reduce harm. High-risk scenarios should be tested deliberately rather than waiting for them to appear during a pilot.
5. Who may be underserved or harmed?
Performance and acceptability may differ across language, culture, disability, age, geography, clinical complexity, and access to technology. Evaluation should examine these differences and document where evidence remains limited.
6. Will it work in the actual setting?
A technically capable tool can fail because it does not fit workflow, creates unclear responsibilities, lacks trusted local information, or adds burden. Implementation outcomes—including feasibility, acceptability, adoption, fidelity, and sustainability—belong in the evidence plan.
7. How will performance be monitored?
AI systems, evidence sources, service directories, and user behaviour can change. Governance should define monitoring, incident review, content updates, escalation, privacy responsibilities, and the conditions that trigger reassessment or withdrawal.
Bottom line
Responsible evaluation is not a final checklist completed immediately before launch. It is an evidence pathway that begins with intended use and continues through design, validation, implementation, and monitoring.