The trust layer for enterprise healthcare AI.
Institution-authored medical records. Named-expert attribution. Documented provenance. Rights review prepared for commercial-training diligence.
Foundation models already know medicine. They lack trust infrastructure.
Enterprise healthcare AI teams need outputs traceable to a named institution, investigator, and citation — not a plausible-sounding answer.
Why existing sources fail
Web-scraped corpora, PubMed/PMC, and publisher full-text are each rated Rare or Partial on named-source attribution, institution linkage, and named-expert graphs — the root cause of citation hallucination, pre-print conflation, and shallow clinical context in production systems.
Why this asset is scarce
Buyers are not licensing medical content. They are licensing the infrastructure required to build healthcare AI systems whose outputs can be traced, attributed, and defended — a content asset, an attribution asset, and a provenance asset.
Why it matters now
Enterprise healthcare AI is accelerating into clinical copilots, physician decision support, medical search, and drug discovery — exactly where an unattributable or hallucinated claim is the failure mode.
Delivered deployment-ready
A self-contained evaluation kit, schema documentation, institution validation pack, and rights & provenance memorandum are prepared for direct technical and legal review under NDA.
The archive is a graph of records, attributing experts, contributing institutions, and subject channels — not a flat list of articles. Records connect, where present in source materials, to publication venues, subject taxonomies, disease categories, funding references, institutional entities, and temporal signals. Every figure traces to a query reproducible against the production corpus under NDA.
The operational deployment slice, fully specified.
The same provenance, named-expert attribution, and structured metadata as the archive, concentrated in the period of highest clinical relevance. Licensable standalone or as the recent layer of the full thirty-year infrastructure.
Source-of-claim attribution
Every generated medical statement traceable to a named institution and investigator, at inference time.
Expert grounding
Claims anchored to credentialed authorities with documented institutional affiliation.
Citation-faithful generation
Models cite real studies with resolvable DOIs where present, instead of fabricating references.
Time-aware reasoning
Established consensus separated from emerging findings by source-dated provenance across the window.
Domain retrieval & expert routing
Queries route to the right subject area and the right named authorities across 90 channels.
Multimodal grounding
Clinical and laboratory imagery paired to release text at the source.
Which source supports trustworthy production deployment.
Not which source contains more medical information — which source carries attribution, provenance, and explainability at the record level.
| Capability | Web-scraped | PubMed / PMC | Publisher full-text | This corpus |
|---|---|---|---|---|
| Named-source attribution | Rare | Partial | Partial | Yes |
| DOI / citation linkage | Rare | Yes | Partial | Yes |
| Institution linkage | Rare | Partial | Partial | Yes |
| Named-expert graph | Rare | Rare | Rare | Yes |
| Funding metadata | Rare | Rare | Partial | Yes |
| Publication & embargo timing | Partial | Partial | Partial | Yes |
| Documented rights-review package | Rare | Rare | Rare | Yes |
Enterprise AI failures rooted in source data
| Observed failure | Root cause | What this infrastructure provides |
|---|---|---|
| Citation hallucination | Web corpora mix verified, pre-print, and aggregator content without source-of-record discipline. | Every record originates from a named institution with named researchers, preserved at record level. |
| Pre-print / peer-review conflation | Training data lacks reliable timing and editorial-status signals on biomedical claims. | Source-dated release timing with institutional editorial review; embargo timing where applicable. |
| Shallow clinical context | PubMed/PMC abstracts truncate the explanatory and clinical context of each finding. | Full institutional-release prose: methodology summary, clinical framing, investigator commentary. |
| Rights & licensing exposure | Most large biomedical corpora are scraped or licensed only for non-training research use. | A thirty-year continuous distribution chain from institution to platform, with rights review prepared. |
| Weak source-of-claim attribution | Web-derived corpora rarely preserve named-expert attribution at the record level. | A deduplicated graph of attributing experts linked to records and institutions, intact at source. |
The difference between asserting a claim and defending it.
Question → LLM → Answer
- ✗ Maybe true
- ✗ Maybe hallucinated
- ✗ Impossible to defend in enterprise settings
Question → Named institution + Named investigator + Publication + DOI → Traceable answer
- ✓ Source-grounded
- ✓ Citation-faithful
- ✓ Enterprise-defensible
Each record carries the following schema
Distribution agreements and rights review.
The corpus is held under continuous distribution agreements with leading U.S. academic medical centers — in many cases for the full thirty-year operating period of the archive.
Records originate from the named institution at the moment of publication, prepared by professional science writers and public information officers in consultation with the named researchers, with publication and embargo timing recorded where applicable. A rights review supporting use for commercial training has been prepared for diligence, and the chain of custody from institution to platform is documented at the record level.
Content asset
- Institution-authored medical research corpus
- Thirty years of continuous coverage (1996–2025)
- Recent medical cohort (2020–2025)
- COVID-19 era captured in full
Attribution asset
- Named investigators
- Institutional affiliations
- Expert attribution graph
- Citation intelligence
Provenance asset
- Source-of-record lineage
- Documented chain of custody
- Publication timing and embargo signals
- Rights-review diligence package
Contributing institutions include Mayo Clinic · Johns Hopkins Medicine · Harvard Medical School · Yale School of Medicine · Cleveland Clinic · Washington University in St. Louis · Children's Hospital Los Angeles
Four independent dimensions configure every deployment.
Each dimension may materially affect valuation, exclusivity, update obligations, and future licensing availability.
Content scope
The content surface licensed.
- Full medical archive (1996–2025)
- Recent cohort (2020–2025)
- Subject-channel subsets
- Disease-category subsets
- Institution-specific collections
- Media layers
- Custom query-based extractions
Time scope
The temporal scope licensed.
- One-time snapshot
- Defined historical window
- Single calendar year
- Rolling 24-month window
- Multi-year access term
- Archive plus forward feed
Market scope
Permitted organizational use.
- Single legal entity
- Parent and named affiliates
- Research consortium
- Hospital networks
- Pharmaceutical research
- Medical search platforms
- Government applications
Exclusivity scope
Market protection granted.
- Non-exclusive
- Time-limited exclusive
- Therapeutic-area exclusive
- Disease-category exclusive
- Use-case exclusive
- Geography exclusive
- Customer-segment exclusive
Update cadence — one-time delivery, annual, quarterly, monthly refresh, continuous forward feed, graph refresh, metadata enrichment, on-demand re-cuts — is typically the largest pricing lever after exclusivity.
Representative commercial structures
Examples rather than fixed products.
Evaluation License
Time-limited access for technical, legal, and procurement review.
Attribution & Provenance License
Retrieval and citation rights only. Stepping-stone before training rights are negotiated.
Attribution Infrastructure License
Broader graph-RAG package supporting production retrieval and downstream inference.
Research & Internal Use License
Access for internal analytics, research, benchmarking, and experimentation.
Active Training License
Rights to train, fine-tune, and deploy models using the licensed corpus.
Strategic Category License
Custom structure combining exclusivity, deployment rights, update streams, and market protection.
Exclusivity framework
Exclusivity may be granted independently across one or more dimensions — industry, application class, therapeutic area, disease category, geography, customer segment, or model class. It is valued independently from access rights: the value of exclusivity reflects future licensing opportunities that become unavailable once protected access is granted, and exclusivity premiums may materially exceed the underlying content-access fee.
Clinical AI Exclusive
Pharmaceutical Research Exclusive
Global Medical Exclusive
Prepared for technical and legal review.
The diligence package includes a rights & provenance memorandum, an institution validation pack, schema documentation, a representative records sample, and the expert-deduplication methodology — alongside a self-contained evaluation kit with a defined retrieval-and-attribution task and reference output, allowing technical teams to benchmark immediately.
info@datawiselabs.ai