Learn how InferTrust™ creates sealed performance records for ongoing AI device monitoring, adverse event investigation, and FDA post-market surveillance reporti
Post-market surveillance is a regulatory requirement for all medical devices, and AI/ML-based devices face unique monitoring challenges. Unlike traditional devices whose performance characteristics are fixed at manufacturing, AI devices may exhibit performance changes due to distributional shift in input data, model degradation over time, or intended algorithm updates. InferTrust™ (Patent Pending) FDA Enforcement creates sealed performance records that support the full spectrum of post-market surveillance activities.
InferTrust™ captures performance metrics at every inference point: confidence scores, decision distributions, escalation rates, and processing characteristics. These metrics are sealed into the decision chain with the same cryptographic guarantees that apply to individual inference records. Manufacturers can analyze performance trends across time periods, patient populations, clinical sites, and model versions with verifiable data. When performance metrics deviate from expected baselines, the sealed records document both the deviation and the model's behavior leading up to it.
When an adverse event involves an AI medical device, manufacturers must determine whether the algorithm contributed to the outcome. InferTrust™ decision records provide the definitive evidence for this determination: the exact model version that processed the case, the confidence score it assigned, whether the case was flagged for clinical review, and whether the clinician accepted or overrode the AI recommendation. This evidence supports both internal investigation and FDA MDR (Medical Device Report) submissions.
Manufacturers of higher-risk AI devices may be required to submit periodic safety update reports (PSURs) or post-approval studies. InferTrust™ provides the verified performance data that these reports require: aggregate performance metrics, subpopulation analysis, comparison against pre-market validation benchmarks, and documentation of any algorithm modifications made during the reporting period. Because this data carries cryptographic provenance, regulatory reviewers can trust its accuracy without additional verification.