Medical False Positives on Foundry: Methods and Safeguards

A clinical notes assistant based on Microsoft Foundry can block a legitimate medical request with a content_filter error. The diagnosis goes through guard annotations to locate the category and severity of the damage, and the blocked side. The correction is made via a custom guard by adjusting a targeted threshold while maintaining self-harm at the strictest level. Validation combines test sets, Azure Monitor metrics, and auditing, with precautions against inappropriate solutions and compliance risks.

Eliminate false remedies and establish operational safeguards

Several common solutions do not resolve a medical false positive in Microsoft Foundry: blocklists only add additional blocks, and an asynchronous filter only changes the timing of the block without altering its nature. The activity log is intended for auditing the control plan, not for content diagnosis. In a clinical context, it is imperative not to loosen self-harm thresholds without prior compliance review. When logging annotations, care must also be taken to avoid any leakage of confidential data.

Identify the precise cause of the block in Foundry

Diagnosing an unjustified block relies on analyzing the guard annotations returned by API calls, which help distinguish between an entry block, a completion block, or a model refusal. It is essential to precisely identify the category of damage involved, its severity, and the side (prompt or completion) where the block occurred. Case study AB-100 illustrates this approach, which involves reading the annotation, qualifying the category and severity, and then targeting the adjustment of the appropriate threshold.

Adjust a custom guard and validate the correction

Since the default guard in Microsoft Foundry is read-only, it is necessary to create a custom guard to correct a false positive by adjusting only the involved threshold on the blocked side. It is important to maintain the highest level of protection against self-harm to ensure clinical safety and governance. Validating the correction involves re-running a batch of clinical tests and a validated self-harm test batch, followed by checking in Azure Monitor that damage detection remains effective and that the number of blocks decreases. An audit of the changes must be conducted in the activity log. This need for targeted adjustment manifests, for example, when an assistant refuses a legitimate clinical question with an HTTP 400 content_filter error, while continuing to correctly block off-topic requests, a false positive frequently encountered by teams in medicine, law, or security.