High Yield Overview
AI in Orthopaedic Radiology
—Fracture Detection
90-95%Sensitivity
—FDA Cleared Tools
—Multiple available
—Common Application
—Wrist, hip fractures
—Role
—Decision support
AI Application Categories
Detection: Fracture identification, abnormality flagging
Measurement: Automated angles, alignment metrics
Planning: Arthroplasty templating, surgical simulation
Prioritisation: Worklist triage by urgency
Key: AI augments clinical capability but requires human oversight
Critical Must-Knows
- AI tools are decision support - clinician remains responsible
- High sensitivity for fracture detection reduces missed injuries
- Best validated for wrist, hip, and chest radiograph applications
- Cannot replace clinical correlation and physical examination
- Regulatory approval (TGA, FDA) required for clinical use
Examiner's Pearls
- "AI assists detection but does not replace clinical decision-making
- "Deep learning uses convolutional neural networks (CNNs)
- "Performance depends on training data quality and diversity
- "Particularly useful for reducing missed fractures in ED
Exam Warning
AI in radiology is an emerging topic. For fellowship exams, understand the basic concepts (machine learning, deep learning), current validated applications (fracture detection), limitations (training bias, cannot replace clinical judgement), and the medicolegal position (clinician responsibility remains).
Core Concepts
AI Terminology
| Term | Definition | Example |
|---|---|---|
| Artificial Intelligence (AI) | Machines performing tasks requiring human intelligence | Any automated image analysis |
| Machine Learning (ML) | Algorithms that improve through experience | Learning from labelled examples |
| Deep Learning (DL) | Neural networks with multiple layers | Convolutional neural networks |
| Convolutional Neural Network (CNN) | Neural network for image analysis | Fracture detection models |
| Training Data | Labelled examples used to teach the algorithm | Radiographs with/without fractures |
| Inference | Applying trained model to new data | Analysing a new patient radiograph |
How AI Learns to Detect Fractures
A deep learning model is trained on thousands of labelled radiographs (fracture vs no fracture). The CNN automatically learns features that distinguish fractures (cortical disruption, angulation, subtle lucent lines) without explicit programming. The model is then validated on a separate test set to assess real-world performance. More diverse training data generally improves generalisation.
Clinical Applications
AI Fracture Detection Performance
| Body Region | Typical Sensitivity | Clinical Utility |
|---|---|---|
| Wrist/hand | 90-95% | Reduces missed scaphoid, metacarpal fractures |
| Hip | 90-98% | Flags occult neck of femur fractures |
| Chest (ribs) | 85-95% | Detects subtle rib fractures |
| Spine | 85-92% | Identifies vertebral compression fractures |
| Ankle | 88-94% | Assists with subtle malleolar fractures |
| Paediatric elbow | 85-92% | Helps with occult fractures |
ED Workflow Integration
AI fracture detection tools integrate into the ED workflow by automatically analysing radiographs and flagging potential fractures. This can reduce missed fractures (particularly important for trainee coverage and high-volume departments), prioritise urgent cases, and provide a 'second read'. The clinician reviews all AI suggestions and makes the final determination.
Performance Metrics
Understanding AI Performance
| Metric | Definition | Clinical Interpretation |
|---|---|---|
| Sensitivity | True positive rate (detects fractures) | High = few missed fractures |
| Specificity | True negative rate (correct negatives) | High = few false alarms |
| PPV | Positive predictive value | Probability positive result is true |
| NPV | Negative predictive value | Probability negative result is true |
| AUC-ROC | Area under ROC curve | Overall discriminative ability (0.5-1.0) |
| F1 Score | Harmonic mean of precision/recall | Balanced performance measure |
Sensitivity vs Specificity Trade-off
In fracture detection, high sensitivity (few missed fractures) is prioritised over specificity. A sensitive AI tool may generate false positives (overcalling fractures) which are easily dismissed by the clinician. Missing a fracture (false negative) has more serious consequences. Most AI tools are tuned for high sensitivity, accepting some overcalling.
Limitations
AI Limitations in Radiology
| Limitation | Explanation | Mitigation |
|---|---|---|
| Training bias | Model reflects training data characteristics | Diverse, representative datasets |
| Out-of-distribution | Poor performance on unusual cases | Clinical oversight, flag uncertainty |
| Black box | Cannot explain reasoning | Explainability research, heatmaps |
| Data quality | Garbage in, garbage out | Quality training data curation |
| Regulatory lag | Approval slower than development | Use only approved tools clinically |
| Integration challenges | Technical/workflow barriers | PACS integration, user training |
Regulatory and Medicolegal
Regulatory Framework
| Aspect | Requirement | Notes |
|---|---|---|
| Classification | Medical device (software) | SaMD - Software as Medical Device |
| TGA approval | Required for clinical use in Australia | Check ARTG registration |
| FDA clearance | Required in USA | 510(k) pathway common |
| CE marking | Required in EU/UK | MDR compliance |
| Clinical validation | Performance data required | Prospective studies preferred |
| Post-market surveillance | Ongoing monitoring | Report adverse events |
Medicolegal Position
The clinician remains legally responsible for the clinical decision, whether or not AI was used. AI is a decision support tool, not a decision-maker. If AI misses a fracture, the clinician is still responsible for the missed diagnosis if they did not exercise appropriate clinical judgement. Documentation should reflect that AI was used as an adjunct, not as the sole basis for the decision.
Future Directions
Emerging AI Applications
| Area | Application | Potential Impact |
|---|---|---|
| Natural language processing | Automated report generation | Efficiency, consistency |
| Multimodal AI | Combined imaging and clinical data | More holistic assessment |
| Federated learning | Training without sharing data | Privacy-preserving improvement |
| Foundation models | Pre-trained, adaptable models | Faster development of new tools |
| Real-time guidance | Intraoperative AI assistance | Surgical precision |
| Outcome prediction | Predict treatment success | Personalised medicine |
Radiologist-AI Collaboration
The future is likely radiologist-AI collaboration rather than replacement. AI handles routine detection and measurement tasks, freeing radiologists for complex interpretation, clinical correlation, and communication. Studies suggest radiologist + AI outperforms either alone for many tasks.
Exam Viva Scenarios
Practice these scenarios to excel in your viva examination
VIVA SCENARIOStandard
EXAMINER
"Your hospital is considering implementing an AI tool for fracture detection on emergency department radiographs. What factors would you consider?"
EXCEPTIONAL ANSWER
Key considerations: (1) Evidence base - Is there published validation data? What is the sensitivity and specificity? Was it validated on a population similar to ours? (2) Regulatory - Is it TGA approved/ARTG listed? This is mandatory for clinical use. (3) Integration - Can it integrate with our PACS and workflow? Who reviews the AI output? (4) Clinical governance - Who is responsible for the final decision? How is AI use documented? What happens if AI misses a fracture? (5) Training - Do clinicians understand how to interpret AI results, including limitations? (6) Cost-benefit - What is the cost versus expected reduction in missed fractures and potential litigation? (7) Quality improvement - How will we audit AI performance in our population? (8) Bias - Does it perform equally across all patient demographics? The clinician always remains responsible for the final clinical decision.
KEY POINTS TO SCORE
Requires TGA approval for clinical use
Published validation data essential
PACS integration and workflow design
Clinician remains legally responsible
Local validation and ongoing audit
COMMON TRAPS
✗Assuming AI eliminates missed fractures
✗Using unapproved tools clinically
✗Over-reliance without clinical correlation
VIVA SCENARIOStandard
EXAMINER
"An ED registrar reviews a wrist X-ray and the AI tool reports 'no fracture detected'. The patient has snuffbox tenderness."
EXCEPTIONAL ANSWER
The registrar should proceed based on clinical judgement, NOT solely on AI output. Management: (1) Snuffbox tenderness is a clinical indicator for suspected scaphoid fracture. (2) AI 'no fracture' does not exclude a fracture - scaphoid fractures are notoriously difficult to detect on initial radiographs (sensitivity approximately 70-80% even for experienced readers). (3) Apply standard scaphoid protocol: immobilise in scaphoid cast/splint, arrange follow-up X-ray in 10-14 days or MRI if available. (4) Document clinical findings and management reasoning. Key principles: AI is decision support, not decision replacement. Clinical correlation is essential. A negative AI result with positive clinical findings requires conservative management. The clinician is responsible for the final decision. Document that AI was reviewed but clinical judgement guided management.
KEY POINTS TO SCORE
Clinical findings override AI output
Scaphoid fractures often occult initially
Treat clinically suspected fracture appropriately
AI sensitivity is not 100%
Document clinical reasoning
COMMON TRAPS
✗Relying solely on AI result
✗Discharging without appropriate follow-up
✗Not documenting clinical reasoning
VIVA SCENARIOStandard
EXAMINER
"You are asked to give a presentation on AI in orthopaedic imaging to your department. What key messages would you convey?"
EXCEPTIONAL ANSWER
Key messages: (1) Current state - AI is increasingly validated for fracture detection (particularly wrist, hip), automated measurements (Cobb angle, alignment), and surgical templating. Multiple tools have regulatory approval. (2) Performance - AI achieves 90-95% sensitivity for fracture detection in validated applications, potentially reducing missed diagnoses. (3) Role - AI is a decision support tool, not a replacement for clinical judgement. The combination of AI + clinician often outperforms either alone. (4) Limitations - AI cannot examine patients, consider mechanism, or integrate the full clinical picture. Performance depends on training data and may not generalise to all populations. (5) Responsibility - The clinician remains legally and ethically responsible for decisions, regardless of AI use. (6) Future - Expect increased integration into workflows, more sophisticated applications (outcome prediction, surgical guidance), and AI-radiologist collaboration models. (7) Implementation - Requires regulatory approval, clinical governance, training, and ongoing audit.
KEY POINTS TO SCORE
AI is decision support, not replacement
High sensitivity but not perfect
Clinical correlation always required
Clinician remains responsible
AI + clinician often outperforms either alone
COMMON TRAPS
✗Over-promising AI capabilities
✗Underestimating implementation challenges
✗Ignoring regulatory requirements
AI in Orthopaedic Radiology Quick Reference
High-Yield Exam Summary
Core Concepts
- •Deep learning uses CNNs for image analysis
- •Trained on labelled examples
- •Validated on separate test data
- •TGA approval required for clinical use
Current Applications
- •Fracture detection (wrist, hip common)
- •Automated measurements (Cobb angle)
- •Arthroplasty templating
- •Worklist prioritisation
Performance
- •Sensitivity 90-95% for fracture detection
- •High sensitivity prioritised (few missed)
- •May have lower specificity (overcalling)
- •AI + clinician better than either alone
Key Principles
- •Decision support, not replacement
- •Clinical correlation essential
- •Clinician remains legally responsible
- •Negative AI does not exclude pathology