OraDigit Labs

Research space for AI-supported medical-imaging workflows.

OraDigit Labs is where we explore and evaluate workflow concepts for order guidance, PET quantification, response tracking, and responsible model development. Research status does not imply clinical validation.

Understand Order HelperUnderstand PET QuantDiscuss research collaboration

What OraDigit Labs is evaluating

Labs keeps demonstrations, research prototypes, and future concepts visible without presenting them as validated clinical products.

Demonstration

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
Review purpose and limits →
Research prototype

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
Review purpose and limits →
Research prototype

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
Review purpose and limits →

Research tracks

These are the deeper areas OraDigit Labs is exploring as the platform matures from workflow tools into clinical AI infrastructure for imaging teams.

Research

Clinical Concordance Analytics

Compare reports, physician interpretations, PET response patterns, and workflow variation to identify agreement, drift, and decision-quality signals.

Research

Workflow Signal Mapping

Identify where imaging workflows fail: unclear orders, missing clinical context, delayed authorization, protocol mismatch, and repeated scheduling friction.

Research

AI-Assisted Clinical Explanation

Explain why an exam, protocol, or quantitative finding needs specific documentation while keeping human review in control.

Future

Clinical AI Dashboard

A platform-level dashboard for order-review activity, PET quantitative confidence, response-tracking trends, and operational insights.

Roadmap from research to reviewed capability

Each concept must pass usefulness, workflow-fit, privacy, safety, and evidence review before its maturity label can change.

Now

Maintain transparent public boundaries

Keep purpose, maturity, limitations, privacy, and human-review responsibilities visible.

Evaluate

Study Order Helper and PET Quant

Define testable questions, governed datasets, review criteria, and evidence requirements.

Next

Evaluate PET Response Tracker

Plan safe longitudinal workflow testing before making performance or clinical claims.

Future

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.

Testable usefulnessEach prototype needs explicit measures before any reduction in confusion, delay, rework, or uncertainty can be claimed.
Human-in-the-loopAI supports review but does not replace clinical judgment or responsibility.
Structured dataFuture analytics require governed inputs, provenance, and approved retention.
Operational contextResearch questions consider scheduling, authorization, imaging protocols, and PET/CT workflows.

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.

Start a research conversation

Last updated: 2026-06-19