AI-Orchestrated
Hematologic
Diagnostics
Reducing bone marrow morphology review from weeks to hours through intelligent workflow orchestration.
The Diagnostic
Capacity Crisis
Bone marrow morphology review is the gold standard for hematologic diagnosis, but manual microscopy creates severe capacity constraints.
40-60% Expert Time on Routine Triage
Hematopathologists spend half their time on straightforward cases that could be pre-screened.
Limited Expert Capacity
Global shortage of trained hematopathologists constrains diagnostic throughput.
2-6 Week Diagnostic Delays
Patients wait weeks for results, delaying critical treatment decisions.
Expert Time Allocation
"Half of expert time is spent on routine work that AI could pre-screen, freeing capacity for complex diagnostics."
Four-Module Diagnostic Platform
Orchestrated AI pipeline that augments expert capacity while maintaining clinical rigor
UScan
Digitizes bone marrow slides at clinical-grade resolution, creating standardized image dataset for AI processing.
Annotation Module
Expert-validated cell type labeling tool that builds ground truth dataset for model training and validation.
Nexus
YOLO/ResNet AI model that detects and classifies bone marrow cells, pre-screening routine cases for expert review.
Compliance
Audit trail and regulatory framework ensuring clinical-grade quality and traceability for medical device compliance.
Seamless Integration: From Sample to Secure Diagnosis
Four modules orchestrate end-to-end AI-assisted diagnostic workflow with expert validation at every critical decision point
UScan
Sample Digitization
Annotation Module
Expert Validation
Nexus
AI Classification
Compliance
Audit & Compliance
Published Research
Academic validation of our AI-orchestrated diagnostic environment through peer-reviewed publication.
P1352: Bone Marrow Morphology Diagnostic Environment Using YOLO/ResNet Artificial Intelligence Model
PMC10430733 • National Center for Biotechnology Information
Abstract
"We developed an AI-based diagnostic environment for bone marrow morphology evaluation using YOLO and ResNet architectures. The system demonstrates significant potential for reducing manual review burden while maintaining clinical accuracy."
Technical Architecture
Clinical Impact Metrics
Validated improvements in diagnostic workflow efficiency and patient care timelines
Patient Journey Timeline
Traditional Workflow
Sample Collection
Bone marrow biopsy performed
Manual Review
Expert microscopy analysis
Final Report
Diagnostic conclusion
AI-Orchestrated Workflow
Sample Collection + Scan
UScan digitization
AI Pre-Screening
Nexus automated triage
Expert Validation + Report
Focused review of flagged cases
Result: 95% reduction in time to diagnosis, enabling earlier treatment and improved patient outcomes
Research Partnership Opportunities
We're seeking academic and clinical collaborators to advance AI-orchestrated diagnostics
Academic Institutions
Universities and research labs advancing AI in clinical diagnostics
- Co-authorship opportunities
- Dataset access for research
- Model improvement collaboration
Clinical Centers
Hospitals and labs implementing AI-assisted workflows
- Platform deployment support
- Clinical validation studies
- Expert panel participation
Technology Partners
AI/ML teams and medical device companies
- Model architecture innovation
- Integration partnerships
- Regulatory compliance support
Express Your Interest
Reach out to discuss research collaboration opportunities
Pioneering Medical Data Tokenization
Santorio is the world's first pilot project to tokenize medical research data—transforming how healthcare knowledge is shared, funded, and accessed globally.
Breaking Data Silos
Medical data is trapped behind institutional walls—but sharing raw patient data isn't the answer. Tokenization creates a new paradigm: the underlying data stays private and protected, while AI models trained on it become accessible. Data contributors benefit without ever exposing sensitive information.
Accelerating Discovery
When researchers worldwide can access AI diagnostic models trained on diverse hematologic data, medical science accelerates exponentially. Rare disease detection improves. Treatment protocols evolve faster. The gap between breakthrough and bedside shrinks from years to months—all while raw patient data never leaves its source.
Data Asset → AI Model
Data becomes a tokenized asset
Ownership recorded on-chain, raw data stays private
AI trains on the data asset
Models improve without exposing underlying data
Researchers access the AI model
Inference endpoints, not raw patient data
Revenue flows to data contributors
Fair compensation for fueling medical AI
Raw Data Exposed
AI Model Access
"Raw medical data never leaves its source. Instead, we tokenize data assets and make AI models available to researchers worldwide. Data contributors get compensated. Privacy is preserved. And medical AI advances faster than ever."
— The Santorio Vision