Human-in-the-Loop (in healthcare AI)
Human-in-the-loop design assigns a qualified, authorized person to review or approve specified AI-supported actions. The checkpoint, reviewer, evidence, and authority should match the action's clinical or operational risk.
Put the evidence in context
Review the intended task, source information, human checkpoints and observed results together. A benchmark or sandbox result has a defined scope; it does not establish suitability, approval or performance for every institution.
Read the full explanationCare Workspace connects healthcare work such as appointments, reviewed reporting and follow-up. Micromeet Cowork supports team work with digital employees and reusable Skills, including meeting summaries, decisions and task handoffs.
Read the full explanationHuman-in-the-loop is a risk control, not a universal rule that every AI action needs a doctor. An institution should identify which outputs can affect diagnosis, treatment, release, patient communication, or another high-risk action, then assign the appropriate authorized clinician or staff member to review, change, reject, or escalate them. Meaningful review requires enough information, authority, and time; an approval click by itself is not sufficient.
Authorized clinicians retain clinical judgment and approval within the institution's workflow. The institution, vendor, and subprocessors may have separate legal, contractual, data, and security duties. The review evidence, audit integrity, and retention required for a deployment depend on its risk, law, policy, architecture, and contract.