AI needs good data. But PubMed has 30 million articles. Which ones matter?
We read them. Our Physician Review Team reviewed over Loading... oncology records. Each one got graded, scored, and annotated. You get 10 quality scores, clinical context, and real takeaways.
Stop wasting your engineers' time. Start training on data that actually works.
AI healthcare startups face a massive problem: drowning in irrelevant, low-quality medical literature while missing the high-impact studies that matter.
Engineers waste weeks filtering noise. Models hallucinate on edge cases. Clinical decision support systems fail because they're trained on broad, unspecific data.
Customize your dataset properties before checkout. If no filters are selected, you will receive the full un-truncated dataset.
Our rigorous physician validation flow ensures that only high-utility, clinically accurate ER records make it to your training pipeline.
Raw records are collected from ClinicalTrials.gov, PubMed, and OpenFDA matching oncology emergency profiles.
Every record passes 10 hardcoded logic rules assessing study type, data completeness, evidence levels, and ER relevance.
Our physicians personally review, annotate, and approve every record on the clinician dashboard.
We deliver structured datasets in CSV/JSON formats with full scorecard validation matrices and custom notes.
A visual representation of the flat database schema and structured physician notes included in every export.
Hardcoded in the validator, not a marketing checklist — every exported record carries its full rule breakdown.
| No. | Rule | What it checks |
|---|---|---|
| 1 | ER Applicability Score | Every record scored 0-10 for real-world emergency department applicability. |
| 2 | Guideline Alignment | Flagged if it contradicts current standard-of-care / ACLS-ATLS-aligned guidelines. |
| 3 | Statistical Integrity | Sample size >=30 and p<0.05 required to pass; underpowered studies are flagged. |
| 4 | Outcome Relevance | Differentiates surrogate endpoints (lab values) from patient-centered outcomes. |
| 5 | Bias Detection | Flags industry-sponsored, single-center, and unblinded studies. |
| 6 | Clinical Plausibility | Compares reported effect sizes to plausible ranges; flags outliers for review. |
| 7 | Actionability | Rates how immediately actionable the finding is in an ER setting (STAT/Routine/N-A). |
| 8 | Evidence Grade | A-F grading, A = meta-analysis down to F = case report / adverse-event report. |
| 9 | Population Fit | Matches study population to typical adult ER demographics. |
| 10 | Recency Weight | Higher weight for studies under 5 years old. |
Every record is verified by active emergency practitioners with clinical clinical-review standards.
- Each record is reviewed by a practicing ER physician with 30+ years of experience
- All physicians on our team maintain active clinical practice in high-acuity environments
- No names listed publicly to protect proprietary review workflows
Early feedback from health AI teams training on our structured oncology emergency datasets.
"Having physician-verified notes saved our engineers hundreds of hours of preprocessing. The quality of our oncology triage bot improved dramatically."
— Lead AI Scientist, HealthTech Unicorn"The 10-rule scorecard combined with ER applicability scores allowed us to filter out noise instantly. Highly recommended."
— Director of Pharmacovigilance, Global PharmaAll tiers are physician-reviewed. Every record ships with its full 10-rule scorecard.
A: Our physician review team personally reviews every record. No automated validation shortcut is used. Each physician has 30+ years of clinical experience.
A: ClinicalTrials.gov, PubMed, and OpenFDA. We're actively expanding to include global registries (EU Clinical Trials Register, WHO ICTRP) for complete global coverage.
A: Yes, use our A La Carte filter tool during checkout to select exactly what you need.
A: Yes. We hear the disclaimer "AI makes mistakes" everywhere. Our datasets are designed to reduce that – by providing physician-verified, high-quality training data that filters out noise and low-evidence studies.
A: CSV and JSON, ready for any AI pipeline. We also offer UDS (Universal Document) format for customers requiring cryptographic verification.
A: Thousands of records across multiple ER specialties, growing weekly. Our pre-September dataset captures the last comprehensive snapshot of public medical literature before AI crawlers are restricted.
A: Enterprise customers (defined as any dataset purchase of $3,500+ or custom volume) receive quarterly updates and priority support.
A: Major internet platforms are implementing new restrictions on AI crawlers starting September 15, 2026. Our pre-September dataset captures the last comprehensive snapshot of public medical literature. After this date, new records will be significantly harder to obtain.
A: Every record is reviewed by a practicing ER physician. We apply 10 quality scores to filter noise. And we cryptographically seal each dataset so you can verify its integrity.
A: Yes. Request a free 50-record Mini dataset. We'll send it within 24 hours.
A: Yes – standard research and AI training license. No commercial resale.
A: Raw PubMed is uncurated noise. We've applied physician judgment to filter, grade, and annotate every record. You get quality, not volume.
We typically respond within 4 business hours.