Medical AI Training Data for Radiology and Clinical Models
Medical AI requires de-identified, clinically meaningful data that links images, reports, findings, and patient context. InfoBay supports radiology AI, clinical NLP, and diagnostic models with DICOM, PDF reports, and specialty records.
The problem
Medical AI models trained on generic web-scraped health content hallucinate on real clinical presentations — they haven't seen paired imaging and reports, haven't seen the specialty breadth of an actual hospital caseload, and can't be de-identified after the fact. A model trained on Western clinical documentation patterns and imaging protocols routinely underperforms on South Asian patient populations and hospital workflows it was never shown, which is the specific gap InfoBay's provider-sourced corpus is built to close. InfoBay's healthcare corpus is collected directly from verified providers, pairing DICOM imaging with PDF reports and clinical records across 20 medical specialties, giving radiology AI and clinical NLP teams data that reflects real patient populations rather than a filtered subset of the public web.
Corpus assets & provenance
Paired imaging and reports at hospital scale
+InfoBay's healthcare corpus includes 2.29M+ patient records and 99M+ medical images and files, spanning modalities including MRI, CT, X-ray, OPG, USG, and ECHO. InfoBay describes this as only Indian hospital corpus with paired DICOM+PDF at this volume, which matters for models that need to learn from an image and its clinical interpretation together rather than an image alone.
Twenty specialties, not a narrow radiology slice
+General-purpose medical AI needs specialty breadth, not just volume: MRI, CT, X-ray, OPG, USG, ECHO, pathology, dermatology, IVF, HIV/infectious disease, oncology, cardiology, gastroenterology, nephrology, general medicine, and outpatient records are represented across the corpus's 20 specialties, with dermatology image-prescription pairs and longitudinal HIV ART and IVF records supporting models that need to reason across a care journey, not a single visit.
De-identification and modality metadata
+Records carry modality, specialty, and source metadata, with de-identification applied before delivery. For vision-language medical models, image-report pairing means a model can learn from the clinical interpretation of a scan, not just the pixels — a distinction that matters for both diagnostic support tools and clinical documentation copilots.
Provenance for regulated clinical AI
+InfoBay's healthcare corpus is collected via formal enterprise agreements, not web-scraped, sourced from verified hospital and clinic partners. Documentation is structured for review under EU AI Act Article 10, India DPDP Act, CCPA, with GDPR-eligible lineage and institutional source records available under NDA for compliance and model-risk teams.
Beyond radiology: clinical NLP and copilots
+The same paired imaging-and-report structure that supports radiology AI also supports clinical NLP and documentation copilots, since PDF reports and structured clinical fields can be used independently of the imaging they're paired with. Prescription and discharge-summary records support tasks like clinical summarization and medication-history extraction, while the specialty and modality metadata attached to every record lets a team scope a training set to a single specialty — dermatology, oncology, cardiology — instead of licensing the full multi-specialty corpus for a narrowly scoped model.
Specifications
| Modality | Format | Volume | Specialty coverage | Provenance |
|---|---|---|---|---|
| Diagnostic imaging | DICOM | 99M+ | MRI, CT, X-ray, OPG, USG, ECHO | De-identified, verified provider source |
| Clinical records & reports | PDF + de-identified metadata | 2.29M+ | 20 specialties | Institutional source, NDA-available |
| Longitudinal records | Structured time-series + PDF | 470K+ HIV ART records, plus IVF care journeys | HIV ART, IVF care journeys | De-identified, verified provider source |
How an engagement works
- 1
Baseline against your target specialty
Every engagement starts by scoping the specialties, modalities, and report-pairing your model needs against a measurable baseline.
- 2
Scope under NDA with institutional review
Healthcare samples are provided under NDA with IRB-equivalent institutional review before a full licensing scope is agreed.
- 3
Deliver de-identified, paired data
Imaging and reports ship de-identified and paired, with modality and specialty metadata attached for downstream QA.
Answers for buyers
FAQ
How many patient records and images does the healthcare corpus include?+
2.29M+ patient records and 99M+ images and files, sourced from verified hospital and clinic partners.
Is imaging paired with clinical reports?+
Yes. DICOM imaging is paired with PDF reports and clinical records, which InfoBay positions as the only Indian hospital corpus with paired DICOM+PDF at this volume.
How is patient data de-identified?+
Records are de-identified before delivery, with modality, specialty, and source metadata retained for review.
Which specialties are covered?+
Twenty specialties, including MRI, CT, X-ray, dermatology, oncology, cardiology, gastroenterology, nephrology, and longitudinal HIV/IVF care.
Can we review a sample before licensing?+
Yes. Healthcare samples are available under NDA with IRB-equivalent institutional review.
What compliance documentation is available?+
Institutional source and modality metadata structured for review under EU AI Act Article 10, India DPDP Act, CCPA, with GDPR-eligible lineage.
Can the healthcare corpus be scoped to a single specialty?+
Yes. Specialty and modality metadata on each record support scoping a training set to a single specialty, such as dermatology or oncology, instead of licensing the full multi-specialty corpus.
Next steps