Research & technology for medical imaging improvement.
OraDigit Labs explores data, engineering and AI/ML questions that support medical imaging quality improvement. We evaluate workflow concepts for order guidance, PET quantification, response tracking, and responsible model development. Research status does not imply clinical validation.
What OraDigit Labs is evaluating
Labs keeps demonstrations, research prototypes, and future concepts visible without presenting them as validated clinical products.
Order Helper
A structured interface for exploring imaging-order context before professional review. No reduction in denials, rework, or authorization time has been established.
- Structured indication context
- Modality, region, and condition logic
- Synthetic or non-identifying information only
PET Quant
Research-oriented organization of glucose, uptake-time, dose-timing, and acquisition context that may affect FDG PET/CT comparability.
- Glucose-aware SUV context
- Uptake-time comparability support
- Professional interpretation required
PET Response Tracker
A longitudinal research workspace for baseline and follow-up PET information. Calculations and categories are not diagnostic conclusions.
- Baseline and follow-up organization
- Percent SUV change context
- Professional interpretation required
Research tracks
These research tracks explore imaging quality, workflow variation, quantitative context and responsible AI. They remain subject to defined evidence and maturity boundaries.
Clinical Concordance Analytics
Compare reports, physician interpretations, PET response patterns, and workflow variation to identify agreement, drift, and decision-quality signals.
Workflow Signal Mapping
Identify where imaging workflows fail: unclear orders, missing clinical context, delayed authorization, protocol mismatch, and repeated scheduling friction.
AI-Assisted Clinical Explanation
Explain why an exam, protocol, or quantitative finding needs specific documentation while keeping human review in control.
Clinical AI Dashboard
A platform-level dashboard for order-review activity, PET quantitative confidence, response-tracking trends, and operational insights.
Imaging AI Quality & Performance Management
An emerging specialized capability within Technology & Research, supporting OraDigit’s medical imaging quality-management focus. Five quality domains guide evaluation, with local validation and continuing surveillance across them. Deploying AI is not the end of AI quality management. An algorithm can perform technically as designed and still fail to create operational value because routing latency, interfaces, delayed results, workflow mismatch, adoption, scanner/protocol or local population differences, data quality and monitoring affect the complete system.
Clinical Performance
Consider task-appropriate performance, such as sensitivity/specificity, false alerts and missed findings, including finding severity and variation by scanner, protocol, facility or patient group. Compare real-world and validation performance with suitable definitions and data. Any evaluation needs agreed scope, evidence and qualified review; this does not imply independent medical-device validation by OraDigit.
Workflow Integration & Latency
Follow the pathway: image acquired → routed → AI processed → result displayed → clinician reviews → action taken. Review routing and processing time, result arrival and availability before report completion, workflow interruptions and downstream action time.
User Adoption & Human Factors
Review utilization, alert review, clinician acceptance, training, trust, workflow burden and appropriate response. Technology, workflow, people, data, measurement and outcomes need evaluation together.
Safety & Data Integrity
Consider DICOM completeness, missing images/series, RIS/PACS and study reconciliation, software versions, interface behavior, repeat-scan investigation, failure detection and corrective action under organizational quality governance.
Financial & Operational Value
Value realization includes patient safety, clinical/operational performance, reliability, utilization and eligible use volume, time to review/action, avoided delays, capacity, costs, implementation burden and sustainability. Documentation/coding, reimbursement readiness or length-of-stay effects may be relevant in an agreed scope. No payment, reimbursement eligibility or financial-return outcome is promised.
Local Validation & Post-Deployment Surveillance
Regulatory clearance or vendor validation does not eliminate the need to understand local real-world performance. Local validation and continuing performance surveillance may be necessary components of responsible AI quality management, depending on use, risk, governance and applicable requirements; this is not a universal legal mandate.
Possible work includes local baseline evaluation and stratification, real-world and workflow monitoring, drift/performance surveillance, review of adverse or unexpected outcomes, software/version changes and periodic reassessment. Availability and responsibilities require agreement; no current surveillance platform or active managed-service contract is implied.
OraDigit does not certify AI safety, independently authorize clinical deployment or provide regulatory approval. A quality assessment does not replace organizational clinical governance or qualified professional judgment. Our engagement framework connects evaluation to a defined problem, baseline, measured result and continued monitoring.
Roadmap from research to reviewed capability
Each concept must pass usefulness, workflow-fit, privacy, safety, and evidence review before its maturity label can change.
Maintain transparent public boundaries
Keep purpose, maturity, limitations, privacy, and human-review responsibilities visible.
Study Order Helper and PET Quant
Define testable questions, governed datasets, review criteria, and evidence requirements.
Evaluate PET Response Tracker
Plan safe longitudinal workflow testing before making performance or clinical claims.
Concordance research
Explore report variation only after governance, privacy, and validation requirements are approved.
How we judge lab work
OraDigit Labs prioritizes concepts that can be evaluated transparently and safely against defined workflow questions.
Interested in a bounded research collaboration?
OraDigit welcomes conversations about responsible workflow evaluation using synthetic or appropriately governed data. A conversation does not imply clinical deployment or a production commitment.
Last updated: 2026-10-05