Orthopedic Surgery and Physical Medicine

Orthopedic AI received FDA clearance earlier than most specialties. OsteoDetect for wrist fracture detection was cleared in 2018, followed by multiple fracture detection systems now deployed in high-volume emergency departments. Robotic surgery systems improve component positioning precision in joint replacement, though long-term outcome data is still emerging. Rehabilitation AI offers objective gait analysis and robotic-assisted recovery. This chapter examines fracture detection, surgical planning, robotic assistance, and rehabilitation applications where evidence supports clinical adoption.

Learning Objectives

After reading this chapter, you will be able to:

Evaluate AI systems for fracture detection on radiographs and CT
Understand AI applications in preoperative planning and surgical navigation
Assess robotic-assisted orthopedic surgery systems and their evidence base
Navigate rehabilitation robotics and gait analysis AI applications
Recognize performance limitations in pediatric and complex fracture cases
Apply evidence-based frameworks for orthopedic and PM&R AI adoption
Critically assess vendor claims for surgical planning and robotic systems

Essential for orthopedic surgeons, physiatrists, sports medicine physicians, physical therapists, and musculoskeletal care teams.

Chapter Summary (TL;DR)

The Clinical Context:

Orthopedics and physical medicine generate substantial imaging data (X-rays, CT, MRI) and functional assessment data, creating opportunities for AI pattern recognition. Unlike many specialties, orthopedics has FDA-cleared AI systems deployed since 2018, providing real-world performance data beyond retrospective studies.

What Works Well:

Application	Systems	Evidence Level	Key Benefit
Fracture detection (radiographs)	Imagen OsteoDetect, Aidoc, Zebra	Strong	90-95% sensitivity for common fractures
Pediatric buckle fractures	OsteoDetect	Moderate	Reduces missed subtle wrist fractures
Joint replacement planning	Multiple vendors	Moderate	Improved component positioning consistency
Robotic arthroplasty	Mako, ROSA	Moderate	Reduced outliers in alignment

What’s Emerging:

Application	Status	Notes
Rehabilitation robotics	Clinical use	Post-stroke, SCI, orthopedic rehab
Gait analysis AI	Research to clinical	Objective assessment, limited adoption
Surgical navigation	Mature	Improved precision, learning curve
Sports injury prediction	Research	Not validated for clinical use

Critical Insights:

Fracture detection AI is mature. First FDA clearance in 2018, multiple systems deployed in high-volume EDs.
Pediatric applications require caution. Growth plates create false positives, AI must be trained on pediatric data.
Robotic surgery improves precision but not necessarily outcomes. Better radiographic alignment, long-term outcome data still emerging.
Societies lack comprehensive AI guidelines. AAOS and AAPM&R provide educational content but not formal clinical practice guidelines.

The Bottom Line:

Orthopedics has mature AI applications for fracture detection and surgical planning. Fracture detection AI should be considered for high-volume emergency departments to reduce missed injuries. Robotic surgery systems improve component positioning consistency but require significant capital investment and learning curves. Rehabilitation AI offers promise for objective functional assessment. Implementation should focus on validated use cases with clear clinical workflows.

Medico-Legal Considerations:

AI does not replace radiologist interpretation; orthopedist remains responsible for diagnosis
Document AI assistance in surgical planning notes
Understand robotic system limitations and manufacturer liability
Pediatric fracture AI has higher false positive rates; clinical correlation essential
Obtain informed consent when using robotic systems

Essential Reading for Orthopedic Practitioners:

Lindsey et al. (2018) on FDA approval of OsteoDetect wrist fracture AI
Multiple RCTs on Mako robotic-assisted knee arthroplasty outcomes
AAOS educational resources on AI integration in orthopedic practice

Introduction: AI in Musculoskeletal Medicine

Orthopedic surgery and physical medicine have embraced AI applications across multiple domains:

Orthopedic surgery: - Fracture detection on radiographs - Preoperative planning for joint replacement - Robotic-assisted surgical systems - Spinal instrumentation planning

Physical medicine and rehabilitation: - Gait analysis and movement assessment - Rehabilitation robotics - Functional outcome prediction - Assistive technology optimization

Why orthopedics is well-suited for AI:

High-volume standardized imaging: Plain radiographs are uniform, DICOM-compliant, and abundant
Quantifiable outcomes: Joint alignment, fracture union, and functional scores are measurable
Procedural reproducibility: Joint replacement has standardized steps amenable to optimization
Objective functional data: Gait analysis, range of motion, and strength are quantifiable

Unique challenges:

Pediatric anatomy: Growth plates mimic fracture lines, requiring age-specific training data
Hardware artifacts: Orthopedic implants create imaging artifacts that confuse algorithms
Complex fractures: Comminuted fractures with multiple fragments challenge detection algorithms
Individual anatomy variation: Patient-specific bone morphology affects surgical planning accuracy

This chapter examines evidence-based AI applications in orthopedics and rehabilitation, with emphasis on deployed systems rather than theoretical applications.

Part 1: Fracture Detection AI

The Clinical Problem

Fracture detection errors occur in 3-10% of emergency department radiographs, leading to delayed treatment, patient harm, and medicolegal liability. Subtle fractures (buckle fractures, stress fractures, rib fractures) are particularly prone to being missed on initial interpretation.

Could AI reduce missed fractures?

FDA-Cleared Fracture Detection Systems

Imagen OsteoDetect (First FDA-cleared orthopedic AI, 2018):

Indication: Distal radius fracture detection on wrist radiographs
Performance: 90-93% sensitivity for adult wrist fractures
Workflow: Flags suspicious radiographs for prioritized review
Deployment: Emergency departments, urgent care centers

Aidoc Fracture Detection:

Indications: Spine, extremity, rib fractures
Multi-site capability: Detects fractures across anatomic regions
Integration: PACS-integrated, automatic triage

Zebra Medical Vision (Now part of Nanox):

Bone health analysis: Fracture detection + osteoporosis screening
Vertebral compression fractures: Particularly strong for spine
Population health: Can screen incidental findings on chest/abdominal CT

Performance Data

What fracture AI does well:

Fracture Type	Sensitivity	Key Evidence
Distal radius buckle fractures	92-95%	Reduces missed pediatric injuries
Vertebral compression fractures	88-92%	Detects incidental findings on CT
Rib fractures	85-90%	Particularly for trauma series
Proximal femur fractures	90-95%	High stakes, clear benefit

Limitations and failure modes:

Pediatric growth plates:
- Unfused growth plates appear as lucencies similar to fractures
- False positive rates 15-25% higher in children <12 years
- Requires pediatric-specific training data
Comminuted fractures:
- Multiple fragment patterns may confuse algorithms
- 10-15% lower sensitivity for complex fractures vs. simple fractures
Image quality dependence:
- Portable radiographs have lower AI performance
- Motion artifact significantly reduces accuracy
- Overlying hardware or casts obscure anatomy
Anatomic coverage gaps:
- Many systems trained on specific anatomic regions (wrist, spine)
- Performance drops for anatomic sites not in training data

Clinical Implementation

Workflow integration:

1. Triage mode: - AI flags suspicious studies for priority review by radiologist - Reduces time to diagnosis in high-volume EDs - Does not replace radiologist interpretation

2. Second reader mode: - AI provides concurrent read alongside radiologist - Highlights areas of concern for review - May reduce oversight errors

3. Alert mode: - Critical findings (displaced fractures) trigger immediate alerts - Orthopedic team notified for urgent cases

Evidence for clinical benefit:

Time to diagnosis: Reduced by 20-40% in high-volume settings
Missed fracture rate: 15-30% reduction in some studies
Radiologist confidence: Increased, particularly for junior radiologists
No RCT evidence yet for improved patient outcomes

When fracture AI is most valuable:

High-volume emergency departments with radiology coverage gaps
Overnight/weekend shifts with limited attending coverage
Urgent care centers without on-site radiologist interpretation
Pediatric EDs for buckle fracture detection

When it’s less useful:

Low-volume settings where all radiographs receive timely expert review
Subspecialized orthopedic practices with experienced surgeons
Settings where MRI or CT is routinely used for fracture diagnosis

Pediatric Fracture Detection: Special Considerations

AI systems trained on adult data perform poorly in children due to:

Growth plate physiology: Unfused physis appears as lucent line
Bone density differences: Lower mineralization in children
Fracture patterns: Buckle, greenstick, and plastic deformation fractures differ from adult patterns
Variant anatomy: Accessory ossicles, normal developmental variations

Recommendation: Use only AI systems with pediatric-specific FDA clearance and validation data. Do not extrapolate adult fracture AI to pediatric populations.

Part 2: Preoperative Planning and Surgical Navigation

AI in Joint Replacement Planning

Modern joint replacement planning software integrates AI for:

1. Implant size prediction: - Automated measurement of joint dimensions from CT or X-ray - Machine learning predicts optimal implant size - Reduces intraoperative size changes by 30-50%

2. Component positioning: - Recommends angles, rotation, and depth - Patient-specific anatomic landmarks - Optimization for biomechanics and longevity

3. 3D surgical planning: - Reconstruction from 2D radiographs or CT - Visualization of bone cuts and component placement - Simulation of postoperative alignment

Evidence base:

Improved planning consistency across surgeons (reduced inter-surgeon variability)
Better correlation between planned and achieved component position
No consistent evidence for improved clinical outcomes (pain, function, implant survival)

The accuracy-outcome gap:

Why don’t more accurate plans lead to better outcomes?

Neutral mechanical alignment is not universally optimal: Some patients do better with kinematic or anatomic alignment
Soft tissue balancing matters more: Ligament tension, not just bone cuts, determines function
Patient factors dominate: BMI, activity level, and expectations predict outcomes more than alignment
Long-term studies needed: Implant survival at 10-15 years is the critical outcome

Robotic-Assisted Joint Replacement

Mako System (Stryker):

The most widely studied robotic orthopedic system, approved for: - Total knee arthroplasty (TKA) - Total hip arthroplasty (THA) - Partial knee arthroplasty (PKA)

How it works:

Preoperative CT scan: Creates 3D model of patient anatomy
Surgical plan: Surgeon plans component size, position, and alignment
Intraoperative registration: Robot registers actual anatomy to preoperative plan
Haptic guidance: Robotic arm guides bone preparation with tactile feedback
Boundary constraints: Robot prevents cuts outside planned boundaries

Evidence from RCTs and meta-analyses:

What Mako improves:

Outcome	Improvement	Evidence Level
Component alignment accuracy	Fewer outliers (>3°)	Strong (multiple RCTs)
Planned vs. achieved position	1-2° more accurate	Strong
Ligament balancing	More consistent	Moderate
Blood loss	Slight reduction	Weak (conflicting data)

What Mako does NOT clearly improve:

Outcome	Finding	Evidence Level
Patient-reported outcomes	No difference at 1-2 years	Strong (RCTs)
Implant survival	No difference yet (limited long-term data)	Weak
Complication rates	Similar to conventional	Moderate
Operating time	15-30 min longer (learning curve)	Strong

Cost considerations:

Capital equipment: $500,000-1,000,000
Per-case cost: $1,000-1,500 (disposables, maintenance)
Learning curve: 20-30 cases to match conventional surgery efficiency
Reimbursement: No additional payment in most systems

ROSA System (Zimmer Biomet):

Similar concept to Mako, different manufacturer
Evidence base smaller but growing
Some studies suggest similar outcomes to Mako

Part 3: Rehabilitation Robotics and Gait Analysis

The Clinical Need in Physical Medicine

Physical medicine and rehabilitation face challenges in:

Subjective assessments: Manual muscle testing, visual gait observation have poor inter-rater reliability
Limited therapy intensity: Insurance constraints limit in-person PT sessions
Adherence monitoring: Home exercise programs poorly tracked
Outcome measurement: Patient-reported outcomes subject to placebo and recall bias

AI offers potential for: - Objective, quantitative functional assessment - High-intensity repetitive practice via robotics - Home-based monitoring and feedback - Personalized rehabilitation progression

Rehabilitation Robotics

Post-Stroke Upper Extremity:

InMotion ARM/HAND (Bionik Laboratories): - Robotic device for arm and hand rehabilitation - Adaptive difficulty based on patient performance - FDA-cleared for stroke, brain injury, spinal cord injury - Evidence: Modest improvements in motor function vs. conventional therapy

Armeo Power (Hocoma): - Exoskeleton for upper extremity rehabilitation - 6 degrees of freedom, adaptive support - Gaming interface for engagement - Used in inpatient and outpatient settings

Evidence for upper extremity robotics:

Meta-analyses show: - Small to moderate effect sizes for motor recovery (standardized mean difference 0.3-0.4) - Most effective when combined with conventional therapy - Benefit greatest in subacute phase (1-6 months post-stroke) - Not superior to equal intensity conventional therapy

Lower Extremity and Gait Training:

Lokomat (Hocoma): - Robotic-assisted treadmill training - Body weight support + robotic leg orthoses - Widely used for stroke, SCI, cerebral palsy

Ekso GT (Ekso Bionics): - Wearable exoskeleton for gait training - Variable assistance levels - FDA-cleared for stroke, SCI

Evidence for gait robotics:

Increased training intensity vs. manual assistance
Objective gait parameters (stride length, symmetry) improve
Functional outcomes (walking speed, independence) similar to conventional therapy
Cost-effectiveness unclear (high capital and maintenance costs)

Spinal Cord Injury Rehabilitation:

Robotics for SCI has mixed evidence: - Incomplete SCI (AIS C-D): Some benefit for gait training - Complete SCI (AIS A-B): Limited functional gains - Cardiovascular and psychological benefits regardless of functional recovery

AI-Enhanced Gait Analysis

Traditional gait analysis: - Requires dedicated motion capture laboratory - Multiple cameras, force plates, EMG sensors - Time-intensive, expensive ($500-2,000 per analysis) - Limited to research and specialized centers

AI innovations:

1. Computer vision gait analysis: - Single video camera captures walking - AI extracts joint positions, angles, temporal parameters - Validates against gold-standard motion capture - Accuracy: 85-95% correlation with laboratory measures

2. Wearable sensor integration: - IMUs (inertial measurement units) on key body segments - Machine learning classifies gait patterns - Real-time feedback for gait retraining - Home and community monitoring

3. Smartphone-based assessment: - Accelerometer and gyroscope data during walking tests - AI predicts fall risk, gait speed, stride variability - Scalable to large populations - Limited accuracy compared to dedicated systems

Clinical applications:

Use Case	Technology	Evidence	Limitations
Parkinson’s gait monitoring	Wearable sensors	Moderate	Does not replace clinical exam
Post-operative TKA gait	Computer vision	Emerging	Not yet standard of care
Fall risk prediction	Smartphone-based	Weak	High false positive rates
Stroke gait asymmetry	Wearable sensors	Moderate	Requires PT interpretation

Barriers to adoption:

Reimbursement: Limited CPT codes for AI-based gait analysis
Workflow integration: Separate assessment outside usual PT workflow
Interpretation training: PTs need training to use quantitative gait data
Validation: Many systems lack large-scale clinical validation

Part 4: Sports Medicine and Injury Prevention

Injury Risk Prediction

Theoretical applications:

ACL injury risk from movement screening
Stress fracture prediction from training load
Shoulder injury risk in overhead athletes
Return-to-play decision support

Reality check:

Most sports medicine AI is in research phase:

ACL injury prediction: - Multiple studies using video analysis + ML to predict injury risk - Sensitivity typically 60-75%, specificity 70-85% - Too low for clinical screening: Would flag hundreds of athletes unnecessarily - No evidence that prediction leads to effective prevention

Training load monitoring: - Wearable sensors track volume, intensity, recovery - Machine learning identifies injury risk patterns - Used by professional teams, limited evidence for injury reduction - Confounding factors: Genetics, nutrition, sleep, psychosocial stress

Current status:

Useful research tool for understanding injury mechanisms
Not yet validated for individual athlete screening
May have role in population-level injury surveillance

Return-to-Play Decision Support

Objective functional testing:

AI can enhance return-to-play assessment by: - Comparing injured to uninjured limb symmetry - Tracking recovery trajectory vs. population norms - Integrating multiple test results (strength, ROM, balance, sport-specific tasks)

Evidence:

Improves consistency of RTP decisions
May reduce re-injury rates (data limited)
Does not replace clinical judgment about psychological readiness, sport demands

Part 5: Implementation Challenges in Orthopedic AI

Integration with Radiology Workflow

Who interprets fracture AI results?

Confusion about roles creates implementation barriers:

Emergency department model: - Radiologist interprets radiograph, orthopedist treats fracture - AI alerts both radiologist and ED physician - Risk of alert fatigue if both groups receive redundant notifications

Urgent care model: - No radiologist on-site, images read remotely - AI provides provisional read pending radiologist review - Orthopedist or ED doc makes initial treatment decision

Liability considerations:

If AI flags fracture but radiologist reads as normal, who is responsible?
If AI misses fracture (false negative), is this a system failure or radiologist oversight?
Malpractice attorneys will argue both parties (radiologist and orthopedist) should have detected it

Best practices:

AI is a triage/prioritization tool, not diagnostic
Radiologist final interpretation remains standard of care
Document AI alerts in radiology report
Orthopedist should not treat based solely on AI flag without radiologist confirmation

Robotic Surgery Learning Curve

Adoption challenges:

Capital cost barrier: - $500K-1M upfront investment - Maintenance contracts $50-100K/year - Disposable instruments $1,000-1,500 per case - Difficult to justify without volume

Learning curve: - First 20-30 cases: Longer operative time, limited benefit - 30-50 cases: Approach conventional surgery efficiency - 50+ cases: Consistent efficiency and accuracy

Surgeon resistance: - Experienced surgeons may resist change (“I already get good results”) - Concern about de-skilling: Reliance on robot reduces manual surgical skills - Marketing vs. evidence: Patients request “robot surgery” without understanding benefits

Evidence-based approach:

Review published RCT data for your specific procedure
If considering adoption, plan realistic volume to justify cost
Invest in structured training (manufacturer courses, proctored cases)
Track outcomes prospectively to validate benefit in your practice
Informed consent should include lack of proven outcome superiority

Data Privacy in Rehabilitation Monitoring

Home-based rehabilitation robotics and monitoring:

Wearable sensors collect continuous movement data
Video-based gait analysis records patient in home environment
Data transmitted to cloud platforms for AI processing

Privacy concerns:

HIPAA compliance: Is PHI adequately protected?
Video data: Home videos may capture family members, living conditions
Third-party access: Device manufacturers, AI vendors may access data
Data ownership: Who owns gait data, movement data?

Best practices:

Use only HIPAA-compliant platforms
Obtain explicit consent for video recording
Minimize data collection to clinically necessary information
Ensure data deletion policies align with medical record retention requirements

Part 6: Professional Society Guidelines and Position Statements

American Academy of Orthopaedic Surgeons (AAOS)

AAOS has engaged with AI through educational programming but has not published comprehensive clinical practice guidelines for AI in orthopedic surgery as of early 2025.

AAOS Position on AI (Educational Focus)

Key areas of AAOS AI engagement:

Annual meeting programming: Sessions on fracture detection AI, robotic surgery, machine learning in orthopedics
Educational resources:
- Webinars on AI fundamentals for orthopedic surgeons
- Coding and reimbursement guidance for robotic procedures
- Medicolegal considerations in AI-assisted surgery
Research support: AAOS Research Department tracks emerging AI applications
Advocacy:
- Supports appropriate FDA oversight of surgical AI
- Advocates for reimbursement models that recognize value of precision surgery
- Emphasizes surgeon control over AI recommendations

What AAOS has NOT published: - Formal clinical practice guidelines on when to use fracture detection AI - Standards for robotic surgery training and credentialing - Quality metrics for AI-assisted procedures

Why guidelines are limited: - Evidence base still evolving - Heterogeneity of AI applications across orthopedic subspecialties - Lack of consensus on outcome metrics for AI benefit

American Academy of Physical Medicine and Rehabilitation (AAPM&R)

AAPM&R addresses AI through the lens of rehabilitation medicine and functional restoration.

Key focus areas:

Rehabilitation robotics integration: Education on appropriate patient selection
Objective outcome measurement: AI for quantifying functional gains
Telerehabilitation: AI-enhanced remote monitoring and coaching
Assistive technology: Machine learning for device optimization (prosthetics, orthotics)

Position themes:

AI should augment clinician assessment, not replace it
Patient-centered outcomes (independence, quality of life) matter more than isolated metrics (gait speed)
Equity considerations: Expensive robotic therapy may exacerbate healthcare disparities
Interdisciplinary collaboration: AI tools require PT, OT, physician, and engineer input

American Physical Therapy Association (APTA)

APTA has published guidance on technology integration in physical therapy practice.

Core principles:

Clinical decision-making authority: PT retains responsibility for all clinical decisions
Evidence-based adoption: AI tools should have published validation for intended use
Patient autonomy: Patients should be informed when AI is used in their care
Professional development: PTs need training to interpret AI outputs

Specific AI applications addressed:

Wearable sensor data interpretation
Telehealth AI assistants
Gait analysis algorithms
Rehabilitation robotics dosing

American Association of Hip and Knee Surgeons (AAHKS)

AAHKS has addressed robotic joint replacement through educational programming.

Guidance themes:

Technology is a tool: Robotic systems do not replace surgical skill and judgment
Evidence-based adoption: Review RCT data, not just marketing materials
Cost-effectiveness: Institutions should perform cost-benefit analysis
Surgeon training: Manufacturer training is necessary but not sufficient; ongoing education essential

Multi-Society Consensus Principles for Orthopedic AI

While individual societies have not published comprehensive guidelines, common themes emerge:

Patient Safety: - AI systems should have FDA clearance or equivalent regulatory approval - Local validation recommended before clinical deployment - Maintain human oversight for all clinical decisions

Evidence Requirements: - Peer-reviewed publication of performance data - External validation beyond development site - Outcomes beyond radiographic accuracy (functional outcomes, patient satisfaction)

Professional Responsibility: - Physicians remain liable for all clinical decisions - Document AI assistance in clinical notes - Maintain competence in non-AI techniques (do not become dependent)

Equity and Access: - AI should not exacerbate existing healthcare disparities - Cost-effectiveness considerations important given resource constraints

Transparency: - Patients should be informed when AI is used in their diagnosis or treatment - Vendor algorithm transparency desirable but often limited by proprietary concerns

Check Your Understanding

Clinical Scenario 1: The Missed Pediatric Fracture

Clinical situation: An 8-year-old falls from playground equipment and presents to urgent care with wrist pain. Radiographs are obtained and flagged by Imagen OsteoDetect as “high probability of distal radius fracture.” However, the radiologist interprets the films as showing an unfused distal radial growth plate with no fracture. The urgent care physician is unsure how to proceed.

Question: How should the clinician reconcile the discrepancy between AI alert and radiologist interpretation?

Answer: Trust the radiologist interpretation, but consider clinical correlation and follow-up.

Reasoning:

Pediatric AI limitations: Fracture detection AI has higher false positive rates in children due to growth plate physiology. Unfused physis can appear similar to fracture lines on X-ray.
Radiologist expertise: A pediatric-trained or experienced radiologist can distinguish growth plate from fracture based on anatomic location, pattern, and clinical context.
Clinical correlation: Physical exam matters. Is there focal bony tenderness over the fracture site vs. physis? Is there deformity? Can the child bear weight (for lower extremity) or use the hand (for wrist)?
When in doubt: If high clinical suspicion despite negative radiologist read:
- Consider splinting and orthopedic follow-up in 5-7 days
- Repeat radiographs at follow-up may show subtle fracture line or periosteal reaction
- MRI not typically needed for simple wrist injuries

What to document: - “Radiographs obtained. AI alert for possible fracture. Radiology interpretation: No fracture, normal growth plate. Physical exam shows tenderness over distal radius but no deformity. Discussed with family: placed in volar splint for comfort, orthopedic follow-up arranged in 1 week. If pain worsens or new symptoms develop, return for re-evaluation.”

Lesson: AI alerts do not override radiologist interpretation. In pediatric cases, growth plates are common sources of false positives. Clinical assessment guides management when imaging is ambiguous.

Clinical Scenario 2: Counseling a Patient About Robotic Knee Replacement

Clinical situation: A 68-year-old with end-stage osteoarthritis of the knee is considering total knee arthroplasty. She has researched “robot surgery” online and asks if you offer Mako robotic-assisted TKA. Your institution recently acquired a Mako system. She believes robotic surgery will result in less pain, faster recovery, and longer-lasting implant.

Question: How do you counsel this patient about robotic vs. conventional TKA?

Answer: Provide evidence-based information: robotic improves alignment accuracy, but patient outcomes at 1-2 years are similar.

What to say:

“We do offer robotic-assisted knee replacement with the Mako system. Here’s what the research shows:

What robotic surgery improves: - More accurate component positioning. The robot helps me achieve the planned alignment very precisely. - More consistent results across patients. Less variability in final alignment. - Potentially helps preserve bone and soft tissues with precise cutting.

What’s the same between robotic and conventional: - Pain levels after surgery are similar - Recovery timeline is similar (most patients walk the same day, go home in 1-2 days) - Physical therapy requirements are the same - Long-term implant survival appears similar (though we need more years of data) - Complication rates are similar

Important points: - The robot is a tool that helps me execute the plan. I’m still performing the surgery. - Whether I use the robot or conventional technique, the goal is the same: a well-aligned, balanced, durable knee replacement. - Success depends more on patient factors (your health, bone quality, commitment to rehab) than on robotic vs. conventional technique.

My recommendation: I’m comfortable with both techniques. If you prefer robotic, we can do that. If you’d rather conventional, my outcomes are excellent with that approach too. The most important thing is choosing an experienced surgeon, not the specific technology.”

What NOT to say: - “Robotic is better” (not supported by outcome data) - “The robot does the surgery” (surgeon still controls all decisions) - “You’ll recover faster” (no evidence for this claim)

Lesson: Patients often overestimate the benefits of surgical technology based on marketing. Evidence-based counseling builds trust and manages expectations appropriately.

Clinical Scenario 3: Implementing Fracture Detection AI in Your ED

Clinical situation: You’re an emergency medicine director considering deploying Aidoc fracture detection AI. Your ED has 60,000 annual visits with approximately 8,000 plain radiographs per year. Radiology provides 24/7 preliminary reads, with final reads available within 12-24 hours. The vendor proposes a cost of $40,000/year.

Question: How do you evaluate whether fracture detection AI is worth implementing at your institution?

Answer: Conduct a systematic cost-benefit analysis and assess workflow integration.

Key questions:

1. What is the current missed fracture rate? - Review past 2 years of malpractice cases, patient complaints, delayed diagnoses - If missed fracture rate is already very low, marginal benefit is small - If 10+ missed fractures per year with patient harm, ROI is higher

2. What is the current radiology workflow? - If all radiographs receive real-time attending radiologist interpretation, benefit is limited to “second reader” - If residents or mid-levels provide preliminary reads, AI can reduce errors - If no radiologist coverage overnight, AI provides valuable triage

3. What is the alert workflow? - How will ED physicians be notified of AI alerts? - Will alerts go to radiologists, ED docs, or both? - What is the expected false positive rate and resulting alert burden?

4. Cost-benefit calculation:

Assumptions: - Current missed fracture rate: 6 per year (0.075% of 8,000 radiographs) - Average cost per missed fracture (delayed care, patient harm, legal settlement): $50,000 - AI sensitivity: 92%, meaning it detects 5-6 of the 6 missed fractures - False positive rate: 10%, meaning 800 false alerts per year

Benefit: - Prevent 5 missed fractures × $50,000 = $250,000 in avoided harm/liability

Cost: - AI license: $40,000/year - Radiologist time reviewing 800 false positives: 800 alerts × 2 min/alert = 1,600 min = 27 hours at $300/hr = $8,000 - ED physician time: Minimal if integrated into workflow - Total cost: ~$48,000/year

Net benefit: $250,000 - $48,000 = $202,000/year

Decision: ROI is favorable if assumptions hold. Recommend 6-month pilot.

Pilot protocol: 1. Deploy AI silently for 3 months, track performance without changing clinical workflow 2. Measure: Sensitivity, specificity, false positive rate, time to flagging 3. If performance matches vendor claims, activate alerts for 3 months 4. Measure: ED physician satisfaction, radiology workflow impact, missed fracture rate 5. Re-evaluate annually

Lesson: AI implementation requires systematic assessment of local needs, workflows, and costs. Silent pilots reduce risk of deploying ineffective tools.

Clinical Scenario 4: Rehabilitation Robotics for Stroke Recovery

Clinical situation: You’re a physiatrist treating a 62-year-old man 4 weeks post-ischemic stroke with moderate left hemiparesis (upper extremity MRC grade 3/5, lower extremity 4/5). He has good rehabilitation potential and insurance that covers 40 PT/OT sessions. Your facility has an Armeo Power upper extremity robotic system. The patient’s family asks if he should use the “robot therapy” they’ve heard about.

Question: How do you approach the decision to use rehabilitation robotics vs. conventional therapy?

Answer: Robotics can be one component of a comprehensive program but is not superior to equal-intensity conventional therapy.

Assessment considerations:

1. Patient factors favoring robotics: - Motivated, cognitively intact (can follow gaming interface instructions) - Moderate impairment (MRC 2-4/5): Robotics most beneficial for this severity range - Subacute phase (1-6 months): Window of greatest neuroplasticity - Tolerates intensive repetitive exercise

2. Patient factors against robotics: - Severe impairment (MRC 0-1/5): May not have enough movement to trigger robot assistance - Mild impairment (MRC 5/5): Conventional task-specific training more functional - Cognitive impairment: Cannot engage with gaming interface - Shoulder pain: Robotic movements may exacerbate

3. Resource considerations: - Is conventional therapy slot available at same time, or does robotic access allow more therapy time? - Therapist supervision required: Not autonomous therapy - Session time: 30-45 min robotic + conventional therapy for ADL training

Your recommendation:

“The Armeo robotic system can be helpful as part of your therapy program. Research shows it’s about as effective as the same amount of conventional therapy, not better. The benefit is that it allows high-intensity repetitive practice with adaptive difficulty.

My recommendation: Use the robotics 2-3 times per week as part of a comprehensive program that also includes: - Conventional occupational therapy for functional tasks (dressing, eating, grooming) - Task-specific training (reaching, grasping real objects) - Home exercise program

The robot is a tool, not a magic bullet. Your recovery depends more on total therapy intensity, your effort, and neuroplasticity than on whether we use robotics vs. conventional techniques.

Let’s plan to reassess in 4 weeks. If you’re progressing well, we’ll continue. If plateau, we’ll adjust the program.”

What NOT to say: - “Robotics is the most advanced treatment” (implies superiority not supported by evidence) - “The robot will help you recover faster” (no evidence for accelerated recovery) - “This is better than what therapists can do” (undervalues skilled therapist intervention)

Lesson: Rehabilitation robotics is one tool in a comprehensive program. Benefits are from intensity and repetition, not from robotics per se. Patient-centered goal-setting and therapist expertise remain central to rehabilitation success.

Key Takeaways

Clinical Bottom Line for Orthopedic and PM&R AI

Fracture detection AI is ready for deployment. FDA-cleared systems reduce missed fractures in high-volume EDs. False positives are common in pediatric cases.
Robotic joint replacement improves alignment, not necessarily outcomes. Better radiographic precision, similar patient-reported outcomes at 1-2 years. Long-term data needed.
Preoperative planning AI improves consistency. Reduces inter-surgeon variability but does not replace surgical judgment.
Rehabilitation robotics enables high-intensity practice. Evidence shows equivalence to conventional therapy of equal intensity, not superiority. Useful when allows more total therapy time.
Gait analysis AI is emerging. Computer vision and wearable sensors offer objective assessment. Not yet standard of care; limited reimbursement.
Sports medicine AI is research-stage. Injury prediction and prevention applications lack clinical validation. Return-to-play support shows promise.
Professional societies provide education, not practice guidelines. AAOS, AAPM&R, AAHKS offer educational resources but lack comprehensive AI clinical practice guidelines.
Pediatric applications require special caution. Growth plates create false positives. Use only pediatric-validated AI systems.
Implementation requires workflow integration. Standalone AI tools fail. Integrate with PACS, EHR, surgical planning systems.
Cost-benefit analysis is essential. High capital costs for robotics require realistic volume and outcome justification. Fracture detection AI has favorable ROI in high-volume settings.