How AI Can Support Benefits Decisions Without Giving Medical Advice

Support the decision—not a diagnosis

Benefits questions can sound clinical because they may involve expected use of care, medications, provider access, or coverage for services. The safe boundary is purpose. A benefits-support tool can help a person understand how documented plan terms relate to stated preferences. It should not tell that person what condition they have or what care they should receive.

This boundary should be enforced in product behavior, not left to a disclaimer. Inputs, prompts, outputs, escalation paths, and testing all need to reflect the intended non-clinical purpose.

Explainability is a control, not decoration

FDA guidance on clinical decision support draws an important distinction around whether a user can independently review the basis for a recommendation. The guidance is specific to medical-device regulation and does not automatically classify a benefits platform. Still, the principle is useful: a consequential recommendation is safer when the evidence and reasoning can be inspected.

For benefits decisions, that means showing the source plan term, the employee-provided preference, the assumption made, and any uncertainty. A confident tone should never substitute for evidence.

Sources: [1] U.S. Food and Drug Administration, [2] U.S. Food and Drug Administration

Appropriate uses of AI in a benefits context

• Summarize approved plan language while linking back to the source document.
• Translate technical benefit terms into plain language without changing their meaning.
• Identify missing, conflicting, or stale information for human review.
• Draft an explanation of a deterministic comparison without changing the underlying rules.
• Route unusual or high-risk questions to a qualified human rather than guessing.

Controls a buyer should expect

• A documented intended use and prohibited-use policy.
• Human review for consequential or ambiguous outputs.
• Source citations and a visible distinction between facts, assumptions, and generated explanation.
• Testing for unsupported claims, harmful advice, bias, privacy leakage, and prompt manipulation.
• Monitoring, incident response, version history, and the ability to disable an AI-assisted feature safely.

Sources: [3] National Institute of Standards and Technology

The CHARLES position

CHARLES uses AI as an explanation and evidence-support capability within governed benefits workflows. It does not provide medical advice or predict diagnoses. Configured decision logic, source data, assumptions, and confidence remain distinct from generated narrative.

Regulatory classification depends on intended use and actual product behavior. Organizations should obtain qualified legal and regulatory advice for their specific deployment rather than relying on a marketing label.