Medical devices & diagnostics
AI visibility monitoring for medical devices & IVDs
From continuous glucose monitors to surgical systems and in-vitro diagnostics, patients and clinicians now ask AI which device to use, whether it is safe, and how it compares. percivo.ai monitors those answers, measures them against your instructions-for-use and indications, and surfaces the vigilance and competitive signals your teams need.
Devices have a vigilance problem AI makes bigger
Consumer-facing devices — CGMs, insulin pumps, hearing aids, CPAP machines, home diagnostics — generate enormous forum and social chatter about malfunctions, side effects, and workarounds. When AI synthesizes that chatter into an answer, it can misstate a contraindication, miss a recall, or amplify an unverified failure report. Every one of those is a signal a device manufacturer should be seeing.
The regulatory scaffolding mirrors pharma closely. In the US, medical device reporting (MDR, 21 CFR 803) feeds the FDA's MAUDE database; complaint handling lives in the Quality System Regulation (transitioning to QMSR, aligned to ISO 13485). In the EU, the Medical Device Regulation (MDR 2017/745) and In-Vitro Diagnostic Regulation (IVDR 2017/746) require manufacturer incident reports, field safety corrective actions (FSCA), and field safety notices, recorded in Eudamed. AI-cited forums are a live source of the incidents these systems exist to capture.
What percivo.ai monitors for device and diagnostics teams
- Indication & instructions-for-use accuracy. Whether AI describes your device for the right patients, the right use, and the right contraindications.
- Competitive share of voice. When a patient asks “best CGM” or a clinician asks “which assay for X”, whether AI names you, a competitor, or neither.
- Vigilance & complaint signals. Potential malfunction and adverse-incident mentions in the forums AI cites, routed to your quality and vigilance teams.
- Recall & field-safety currency. Whether AI is repeating outdated safety information or missing a field safety notice.
Combination products sit on the pharma–device boundary
Autoinjectors, prefilled pens, inhalers, and drug-eluting devices are co-regulated as combination products. If your portfolio includes them, the device half carries its own vigilance and accuracy stakes on top of the drug's — and AI answers rarely draw the line cleanly. percivo.ai monitors both halves in one workspace.
Frequently asked questions
What is GEO for medical devices?
Generative engine optimization (GEO) for medical devices is the practice of monitoring and improving what generative AI tools say about a device or diagnostic to patients, clinicians, and procurement teams — and catching device-vigilance signals in the sources those AI answers cite. It is the AI-era equivalent of SEO, adapted to device regulation (MDR, IVDR, MAUDE, complaint handling).
Does percivo.ai cover in-vitro diagnostics (IVDs)?
Yes. IVDs are monitored the same way as devices and drugs: percivo.ai tracks how AI describes the intended use, the population, and the performance claims of an assay, and measures those answers against your validated labeling — while surfacing where competitors are named instead.
How does this relate to medical device reporting and vigilance?
AI answers and the patient/clinician forums they cite are a live source of malfunction and adverse-incident mentions — the same events that feed MDR/MAUDE in the US and manufacturer incident reports and FSCAs in the EU. percivo.ai detects and routes those mentions to your quality and vigilance teams with timestamps; it does not replace your complaint-handling system.
Can it monitor combination products (drug + device)?
Yes — that is a natural fit. Autoinjectors, inhalers, prefilled pens, and drug-eluting devices are co-regulated, and percivo.ai monitors the drug and device halves together in one workspace, tracking accuracy and vigilance for both.
Which AI tools do you monitor for device brands?
ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, and Grok (xAI) — the five that patients, clinicians, and procurement teams actually use, each of which answers and cites sources differently.