1M+ healthcare records across prescription digitisation, diagnostic reasoning, radiology, and pathology, with imaging patients and clinical notes providing symptom, disease, and side-effect grounding.
| Record | Modality | Source | Extracted finding | ICD-10 | Drug link | Status |
|---|---|---|---|---|---|---|
| R-1042 | MRI | PACS | L4-L5 disc herniation | M51.16 | linked | ready |
| R-1043 | Rx | EHR | metformin 500mg BID | E11.9 | linked | ready |
| R-1044 | Path | LIS | adenocarcinoma, grade 2 | C18.9 | linked | ready |
| R-1045 | Note | EHR | HbA1c 7.8% trending | E11.65 | - | Grounding... |
| R-1046 | CT | PACS | RLL nodule 6mm, stable | R91.1 | linked | ready |
| R-1047 | Rx | Scan | atorvastatin 20mg QHS | E78.5 | linked | ready |
| R-1048 | MRI | PACS | left meniscal tear | S83.242A | - | Grounding... |
| R-1049 | Path | LIS | chronic inflammation, mild | K63.89 | linked | ready |
| R-1050 | Note | EHR | shortness of breath on exertion | R06.02 | linked | ready |
| R-1051 | CT | PACS | hepatic steatosis, moderate | K76.0 | linked | ready |
| R-1052 | Rx | EHR | lisinopril 10mg daily | I10 | - | Grounding... |
| Capability | Prescription | Diagnostic | Reports | Drug data | Zstate Healthcare |
|---|---|---|---|---|---|
| Structured digitisation output | ✓ | ✕ | ~ | ✕ | ✓ |
| Clinical reasoning tasks | ✕ | ✓ | ~ | ~ | ✓ |
| Radiology interpretation | ✕ | ~ | ✓ | ✕ | ✓ |
| Pathology interpretation | ✕ | ~ | ✓ | ✕ | ✓ |
| Symptoms and disease context | ~ | ✓ | ~ | ✓ | ✓ |
| Side effects and drug metadata | ✕ | ~ | ✕ | ✓ | ✓ |
| Cross-task medication grounding | ~ | ~ | ~ | ✓ | ✓ |
A large healthcare dataset covering prescription digitisation, diagnostic reasoning, radiology report interpretation, and pathology report interpretation. Built to preserve real medical language, messy clinical inputs, and task-specific outputs.
A strong drug dataset tied to symptoms, diseases, side effects, and medication entities. This turns the core corpus from isolated task data into a more connected substrate for training grounded medical systems.
Messy prescription inputs turned into structured medication signals, dosage understanding, and normalized entities.
Clinical reasoning tasks designed for models that need to connect symptoms, disease hypotheses, and medication context.
Report understanding that can support extraction, evaluation, and downstream medical AI workflows over imaging narratives.
Pathology report coverage for systems that need to reason over findings, impressions, and disease-linked clinical language.
Drug data linked to symptoms, diseases, and side effects that can ground the rest of the clinical corpus and complete the loop.
The corpus is modular. Teams can work with a single workflow family or pull linked subsets spanning extraction, reasoning, and grounding into their training pipeline.
We can package a sample around prescription digitisation, diagnostic reasoning, report interpretation, and the linked drug context layer, depending on what your training pipeline needs first.
