Learning & Assessment
A modern learning and scoring platform for diagnostic imaging
AR includes a purpose-built learning and assessment platform designed for diagnostic imaging education — supporting learners, educators, and training programs with structured feedback, objective scoring, and auditable outcomes.
Unlike generic learning management systems, AR’s learning tools are deeply integrated into real diagnostic workflows. Learners interpret real studies, generate structured reports, and receive meaningful feedback based on both clinical content and measurable accuracy.
Designed for learners
AR allows learners to practice and be assessed using real-world diagnostic studies, not simplified simulations.
Learners can:
- Interpret full diagnostic imaging studies
- Complete structured reports using the same tools used in clinical practice
- Practice independently or complete formal assessments
- Receive objective, consistent scoring aligned to defined criteria
- Track progress over time across cases and competencies
The platform supports both practice mode and exam mode, ensuring learners can train while still working in an authentic environment.
Built for educators
Educators and supervisors gain visibility into learner performance without adding administrative burden.
Educators can:
- Review learner reports alongside the reference (gold standard) interpretation
- See structured scoring breakdowns rather than subjective pass/fail outcomes
- Identify strengths, gaps, and recurring errors across learners
- Provide targeted feedback efficiently
- Ensure consistency across multiple assessors and sites
Because scoring is automated and standardized, educators spend less time marking and more time teaching.
Built on recognized standards
AR’s learning and assessment tools align with widely recognized frameworks for competency-based education and interoperable clinical data:
- Competency-based training principles (e.g., CanMEDS, ASE, CSE)
- Clinical imaging and reporting standards (e.g., DICOM)
- Structured clinical data exchange standards (e.g., HL7 FHIR)
Objective, multi-layered scoring
AR uses a multi-pass scoring approach that reflects how diagnostic competence is actually assessed.
Scoring can include:
- Content accuracy (key findings present or absent)
- Completeness of required report elements
- Consistency with expected diagnostic language
- Measurement validity, where applicable
- Section-level and overall performance summaries
This structured approach reduces subjectivity and helps ensure fairness across learners and cohorts.
Practice and exam workflows
AR supports both formative learning and high-stakes assessment.
Programs can:
- Assign curriculum cases for guided learning
- Deliver timed exams using controlled workflows
- Lock reports on submission for exam integrity
- Automatically score completed exams
- Generate defensible assessment records
Transparent feedback and auditability
Every learner action and scoring outcome is recorded.
This provides:
- Full audit trails for assessments
- Clear documentation for accreditation and program review
- Defensible records for progression decisions
- Transparency for learners reviewing their performance
Nothing is overwritten or silently changed — results remain traceable and reviewable.
Scales from individuals to institutions
The AR learning platform is used by:
- Individual learners
- Academic training programs
- Hospital-based education departments
- Multi-site and multi-tenant organizations
Role-based access ensures learners, educators, and administrators see exactly what they need — and nothing they shouldn’t.
Integrated, not bolted on
Learning and scoring are not separate modules — they are part of the same platform used for clinical reporting.
This means:
- No duplicate systems to maintain
- No artificial training interfaces
- No context switching between “learning” and “real work”
- A smoother transition from training to independent practice
See it in action
If you’re responsible for training, assessment, or credentialing in diagnostic imaging, we’d be happy to show you how AR supports learning without compromising clinical workflows.