The evidence FDA actually accepted.
Every parsed AI/ML 510(k) summary as structured, source-quoted fields — validation design, endpoints, predicate chains — connected to what happened after clearance and how devices got paid.
Presence rates, never pooled.Every figure below is “reported in X of Y audited devices.” Performance values are never averaged across devices — each stays with its analysis unit, task, and a verbatim FDA quote. A device we haven’t parsed yet is queue, never “FDA accepted thin evidence.”
What evidence did FDA accept for devices like yours?
Keyword-based retrieval over the parsed corpus (names, algorithm descriptions, endpoints, source quotes) — not semantic search. Try: · ·
Corpus coverage — Neurology
The hatched segment is our parse queue, not device data. Queued devices never count in any rate below — every denominator is audited devices only.
Original findings from the corpus
Median FDA review time, received → decision
Across all 1,146 matched AI/ML 510(k)s; the slowest decile takes 8.5+ months (p90 ≈ 260 days). Computed from openFDA decision dates — not published anywhere else for this cohort.
PCCP adoption in 2025 AI clearances
Up from 0% before 2023 (5.0% in 2024). Nine in ten AI/ML clearances still file without a Predetermined Change Control Plan — a live gap as FDA pushes the mechanism.
What the audited corpus reports
Evidence reporting
Canonicalized — includes per-finding sensitivities, not just the summary's top-line slot.
“Not reported” is a real finding about what FDA accepted — the summary was audited and does not state it.
Metric presence (canonicalized)
- accuracy1 of 2
Presence only — values are never pooled or averaged across devices. Each measurement lives on its device's page with its analysis unit and verbatim quote.
Median predicate age: 8.9y across 3 of 4 predicate edges (75% datable, dates backfilled from openFDA) — how old each cited predicate was when the child device cleared.
Study design & generalizability
Where FDA review is tightening — and what almost no premarket dataset reports. Each rate is presence over audited devices; a device that describes no clinical study is honestly “not reported” here.
Primary validation is prospective-clinical or a multi-reader design — not a retrospective bench run.
Validation data explicitly independent of the training/development set.
Tested across more than one scanner or device manufacturer.
Performance reported per subgroup, not just an overall number.
One corpus, whole lifecycle
Clearance evidence
2 devices parsed with source-quoted fields
Postmarket signals
66 devices snapshotted · 33 of 66 carry a drift signal
Reimbursement pathways
5 pathways across 4 devices (seed corpus, growing)
Every device page connects all four stages: clearance → evidence → postmarket → payment. Stages without data render as “not yet tracked”, never as empty or fabricated.
Machine access
The same retrieval over MCP — 12 tools across the lifecycle
curl -s https://constat.dev/api/mcp \
-H 'content-type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
"params":{"name":"evidence_search",
"arguments":{"panel":"Radiology","reports_any_sensitivity_metric":true}}}'device_evidence_lookup, evidence_search, predicate_chain, device_postmarket_lookup, reimbursement_lookup, and more. Every extracted field carries a source quote and page — descriptive only, never a compliance judgment.
Follow the corpus as it grows
New parses land weekly, most-recent-first. Get notified as coverage expands and presence rates shift — and be first on the client digest when it ships.