A human-centred service system that helps radiology teams use AI safely, capture AI-human disagreement, support arbitration, transform cases into learning, and build governance-ready protocols.
Mammography • Routine Screening
No recall. Stable dense tissue on comparison to prior.
Suspicious mass, right breast, upper outer quadrant.
Capture AI-human disagreement, override reasons, and daily calibration signals directly from clinical use.
Compare radiologist judgement, AI suggestions, prior evidence, and structured reasoning in one arbitration view.
Turn difficult cases into personal reflection, team calibration material, and CPD-ready learning records.
Radiologists use AI during clinical reading.
System records when judgements differ.
Complex cases structured for fairer review.
Reasoning, overrides, and AI versions logged.
Cases become personal & team learning.
Data supports protocol and calibration.
Record disagreement without adding documentation burden. A streamlined view into your daily AI interactions.
| CASE ID | AI SUGGESTION | STATUS | REASON TAG |
|---|---|---|---|
| #882-A | Mass (88%) | OVERRIDE | Known Prior |
| #883-B | Normal (95%) | AGREE | - |
| #884-C | Calcification (72%) | OVERRIDE |
"No recall. Dense tissue, stable on comparison to prior imaging."
"Suspicious focal asymmetry, right breast, upper outer quadrant."
Bring structure to complex arbitration decisions. Compare initial reads with AI highlighting and prior imaging side-by-side.
Transform disagreement into continuous professional learning. Your personal dashboard for CPD-ready reflection and team calibration.
Build safer, more traceable AI adoption workflows with enterprise-grade governance data.
Support your teams in arbitration, structured learning, and consistent calibration.
Reduce cognitive burden, support clinical agency, and easily log professional development.
Receive structured, contextual feedback from real clinical AI use to improve models.
Turn real-world, anonymised disagreement cases into high-value learning materials.
Monitor AI-human decision patterns, protocol compliance, and systemic risk.
Ready Ology turns difficult disagreement cases into anonymised learning resources for individual radiologists, department teams and CPD-ready professional development.
Track individual reflection, override patterns and learning progress privately.
Review shared, anonymised disagreement cases in monthly calibration meetings.
Export structured reflections, case summaries and learning records for appraisals.
Ready Ology helps organisations move beyond one-off AI use by creating structured data for protocol compliance, model monitoring and continuous service improvement.
"Every disagreement becomes evidence for safer AI deployment."
Radiologists remain the active, final clinical decision-makers in the loop.
Disagreement, clinical reasoning, and exact AI version data are systematically documented.
Cases become high-value learning resources, avoiding cultures of individual blame.
Systemic patterns feed directly into protocol updates, QA, and ongoing AI calibration.
Partner with Ready Ology to build safer, clearer and more learnable AI-assisted radiology workflows.