The premise
Over 1,000 AI-related regulations across 70+ countries — and no single place that compares how regulators govern them.Global AI regulation for medicines is fragmented. AICURIS reads the public output of major regulators, classifies the AI-relevant material with a hybrid semantic-and-keyword model, and lays it side by side so a reviewer can see alignment and divergence at a glance.
The register
Ranked by AI-relevance. Documents in the paper's classified set carry the AI-CLASSIFIED stamp.Each title links to the live source. Because agency pages move or get blocked over time, every row also offers an archived copy — the Internet Archive snapshot from our collection window (~2024). Use it whenever a source link is dead, redirected, or blocked.
Smart search asks an AI model to rank the full corpus for you — one click, not live-as-you-type, and rate-limited to a few searches a minute.
Federated view
One theme, three regulators. Click a theme to filter the register.The point of AICURIS is comparison. For each recurring theme in AI medicines regulation, here is how many documents each authority has produced — a map of where the world's regulators are converging, and where they are silent. Themes are an exploratory keyword grouping for navigation — not part of the paper's formal classification.
Method & performance
A dual-scoring OR-ensemble — semantic similarity for implicit AI, keyword similarity for explicit terminology.Regulatory text discusses AI in two ways: explicitly (naming machine learning, LLMs) and implicitly (describing data-driven methods without the vocabulary). One signal alone misses half of it.
Each document is scored two ways — semantic similarity to a curated reference set of AI-regulatory documents, and keyword similarity across AI, legal and biomedical term banks. A document is flagged AI-relevant if either signal clears its threshold.
The two methods correlate only moderately (≈0.41–0.53), which is exactly why combining them helps — they fail on different documents. Thresholds were chosen by grid search to hold recall high while keeping the review burden manageable.
⚑ Honest limitations
- Recall-only. Precision was not estimated — labelling 99,070 documents by hand was not feasible.
- Confidence varies. ~21% of classified documents are corroborated by both signals; ~65% clear the threshold on a single signal near the decision boundary (higher false-positive risk). Each record shows its tier; filter by confidence in the register.
- Scope: English-language documents; FDA, EMA and WHO; 2019–2025.
- Snapshot. Data reflects the paper's collection window (through 2025); periodic live refresh is on the roadmap.
- Proof-of-concept. It surfaces and structures documents to inform human review; it issues no regulatory judgement.
- This demo reproduces the paper's exact classification for all three agencies — FDA 2,433 · EMA 538 · WHO 17 = 2,988 AI-relevant — sourced directly from the production scoring database.
Recall vs. review burden
Each curve is one scoring signal: as the threshold relaxes, recall rises but more documents need review. The ensemble lets us sit high-left — high recall, low burden.
AI-related regulatory output, by year
Indexed documents per authority, 2019–2025. The slope is the story.
Keyword taxonomy
The keyword arm scores each document against three curated term banks. These are the actual banks.
Reference anchor set
The semantic arm scores each document against this curated set of AI-regulatory guidance (plus one synthetic concept anchor).
About & data
Open access. Cite the paper. Download the corpus.AICURIS is the public companion to a peer-reviewed study. It exists to make the paper's claim testable: that an AI-enabled system can federate AI-related drug regulation across jurisdictions and keep it current.
The full indexed corpus — every document, agency, date and score — is available below for independent analysis, in keeping with the paper's data-availability commitment.
Engineered by Mukesh Pareek (software & validation author). Part of Singh, Pareek, Prokle, Sanchez, Hohgrawe & Auclair, Frontiers in Drug Safety and Regulation, 2026.
