Medical Appliance Technicians
51-9082.00Median wage $48,030/yr11,280 employed (US)Rank #714 of 923 scored · top 77% by substitution
Construct, maintain, or repair medical supportive devices such as braces, orthotics and prosthetic devices, joints, arch supports, and other surgical and medical appliances.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution18
Exposure14
Augmentation37
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
15 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%17
panel mean rating 1.7/5 → substitution pressure 17/100
Technical feasibility todayw 20%8
panel mean rating 1.3/5 → substitution pressure 8/100
Cost vs. human wagew 15%12
panel mean rating 1.5/5 → substitution pressure 12/100
Adoption barriersw 20%inverted — strong barriers lower the score38
panel mean rating 3.5/5 (barrier strength) → substitution pressure 38/100
Sector adoption velocityw 10%12
panel mean rating 1.5/5 → substitution pressure 12/100
Task breakdown (15 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Lay out and mark dimensions of parts, using templates and precision measuring instruments.
51CI 21–80 · exposure 53 · augmentation 38 · importance 4.2/5 · click for rater detail
Lay out and mark dimensions of parts, using templates and precision measuring instruments.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Medical device manufacturers are adopting automated inspection and marking, but adoption is steady rather than rapid due to regulatory validation requirements and the sector's conservative approach to process changes. Most adoption remains in larger firms with dedicated engineering resources. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Medical appliance manufacturing is a small-scale, physical, low-digitization sector with limited AI/robotics adoption for bespoke fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist technicians by overlaying CAD dimensions, highlighting discrepancies, and recommending correction—useful for quality assurance and training—but the core marking and layout task is so straightforward that augmentation offers limited value compared to full automation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | CAD/CAM and digital measurement tools can assist with design and dimensioning, but the physical layout and marking on materials still relies heavily on manual skill. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Laying out and marking part dimensions can be fully automated using computer vision systems, CAD software integration, and robotic marking tools. A 50% time saving at equal quality is easily achievable with AI-guided measurement and automated precision marking, reducing manual layout work to near-zero. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of materials and precision instruments on custom orthotic/prosthetic parts, which current AI systems cannot perform end-to-end; only digital design portions could be assisted.-Robots lack the dexterity for this bespoke fabrication step. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Medical device manufacturing is regulated (FDA, ISO 13485), requiring documented traceability and validation of automated processes. These add implementation friction but do not legally require a human to perform the marking itself—certification of the automation is the barrier. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not explicitly licensed, quality and fit of medical appliances carry liability concerns and require hands-on craftsmanship, creating moderate resistance to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI vision and robotic marking systems are orders of magnitude cheaper than human labor once amortized across production runs. The capital cost is recouped quickly in high-volume medical appliance manufacturing with 24/7 operation potential. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any hypothetical automation (specialized robotics) would be far more costly than a technician's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Computer vision-based dimensional marking and AI-aided layout systems exist in production quality control environments, though deployment in medical appliance manufacturing remains less mature than in general manufacturing. Existing vision and robotics systems can perform this reliably, but sector-specific validation for medical devices adds some friction. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical layout and marking of custom medical appliance parts; this remains a manual craft task with precision measuring tools. |
Read prescriptions or specifications to determine the type of product or device to be fabricated and the materials and tools required.
41CI 30–51 · exposure 38 · augmentation 63 · importance 4.4/5 · click for rater detail
Read prescriptions or specifications to determine the type of product or device to be fabricated and the materials and tools required.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Medical device and appliance manufacturing remains moderately digitized with slower IT adoption than finance or software sectors; most fabrication shops use legacy systems and manual workflows, slowing AI adoption despite technical feasibility. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Medical appliance fabrication is a small-scale, hands-on manufacturing sector with low digitization and slow AI tool adoption relative to information-sector fields. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered document parsing and material/tool recommendation systems can significantly accelerate technician productivity by instantly extracting and highlighting critical prescription details and suggesting required materials, keeping the human decision-maker in the loop for validation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help parse prescription text, cross-reference material databases, or suggest specifications, providing moderate assistance while the technician retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can parse and extract structured data from prescriptions and specifications (type of product, materials, tools) with high accuracy, achieving significant time savings on the interpretation component. However, handling ambiguous, handwritten, or poorly formatted prescriptions and validating against complex clinical requirements still requires human oversight, limiting full end-to-end automation. |
| Task automatability | claude-sonnet-5 | 2/5 | Reading and interpreting a prescription to determine device specs requires domain judgment and physical-world context that AI can partially assist with but not fully execute end-to-end without human verification.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While prescriptions must ultimately be issued by a licensed practitioner, the task of reading and interpreting them for fabrication does not carry strict legal gatekeeping; however, liability concerns and clinical validation requirements create organizational friction that slows substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not strictly licensed sign-off for this sub-task, quality/safety liability for custom medical devices creates strong organizational reluctance to remove human review of specifications. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Cloud-based AI document processing and extraction services are inexpensive per inference (fractions of a cent), making automation substantially cheaper than the loaded wage of a medical appliance technician performing manual review and interpretation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Skilled technician interpretation is still cheaper and more reliable than building/maintaining a specialized AI pipeline for low-volume, highly variable prescriptions. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | OCR and AI document-processing tools deployed in healthcare can reliably extract prescription data, but they remain error-prone on handwritten scripts and require clinical context verification that deployed medical AI systems do not yet reliably perform independently at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product reliably interprets clinical orthotic/prosthetic prescriptions and autonomously determines materials/tools; this remains largely manual with occasional software-assisted spec lookup. |
Polish artificial limbs, braces, or supports, using grinding and buffing wheels.
26CI 24–28 · exposure 16 · augmentation 13 · importance 4.1/5 · click for rater detail
Polish artificial limbs, braces, or supports, using grinding and buffing wheels.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Medical appliance technician work is performed in small clinics, prosthetics shops, and distributed settings with low digitization and limited capital for automation. The sector has not demonstrated meaningful AI adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance fabrication is a small-scale, physical, low-digitization trade with minimal AI/robotics adoption for finishing operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with surface defect detection via computer vision or recommend finishing parameters, but the core motor task—hand-guided polishing with real-time adjustment—remains human-dependent and offers limited scope for augmentation. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to the physical act of grinding and buffing artificial limbs or braces. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Polishing involves fine manual dexterity, spatial reasoning, and judgment about finish quality that requires sensing and real-time adjustment. While automated grinding/buffing machinery exists in manufacturing, the task as stated—performed by technicians on individual custom-fitted devices—requires tactile feedback and adaptive finishing that current AI robots struggle with at acceptable quality and speed. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a physical manual finishing task requiring dexterity, tactile feedback, and fine motor control with grinding/buffing equipment, which current AI systems cannot perform without robotic hardware not generally deployed in this trade. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no legal licensing barriers preventing automation, but customer preference for quality, orthopedic device regulations (FDA 510k), and liability concerns around finish defects on medical devices create moderate organizational friction against rapid automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically governs the polishing step itself, though quality/safety expectations for finished medical devices create some organizational caution, but it's not a hard legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A robotic arm with vision and force feedback suitable for this task would cost tens of thousands to integrate per technician station, plus ongoing maintenance. The technician wage is moderate, and the task is performed in small batches on diverse geometries, making the breakeven point unfavorable for AI substitution. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic substitute exists at scale, so any hypothetical automation would require expensive custom robotics far costlier than a technician's labor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic systems reliably perform end-to-end polishing of orthopedic appliances in production. Existing industrial polishing robots operate in rigid, high-volume settings with standardized geometries; custom limbs, braces, and supports have variable shapes and require human judgment on finish. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed AI or robotic products performing polishing of custom orthotic/prosthetic devices in production; this remains a manual craft skill in appliance shops. |
Bend, form, and shape fabric or material to conform to prescribed contours of structural components.
20CI 10–30 · exposure 13 · augmentation 38 · importance 4.4/5 · click for rater detail
Bend, form, and shape fabric or material to conform to prescribed contours of structural components.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Medical device manufacturing is moderately digitized and cautious about process change; while some larger firms pilot automation, adoption remains limited due to regulatory burden, product customization, and the technical difficulty of generalizable fabric-forming systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance manufacturing is a physical, low-digitization trade with minimal AI adoption for hands-on fabrication tasks; robotics/automation in this niche remains experimental at best. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision and robotic guidance can assist technicians by marking contours, guiding initial folds, or automating straightforward sections, meaningfully reducing manual effort on repetitive shaping steps while the technician retains quality oversight and handles complex or variable geometries. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with design specifications, 3D modeling, or CAD-based contour guidance, but it offers little direct assistance during the physical bending and shaping process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can recognize contours and guide robotic arms, the task requires real-time tactile feedback, material property assessment, and adaptive response to fabric behavior that current AI systems cannot reliably execute end-to-end without substantial human intervention or rework. |
| Task automatability | claude-sonnet-5 | 1/5 | This is manual, tactile fabrication work shaping physical materials (e.g., orthotic/prosthetic components) to precise anatomical contours, requiring physical dexterity and hands-on manipulation that current AI systems cannot perform.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Medical device manufacturing is subject to FDA and quality compliance standards that typically require human verification of fit and form; some facilities may impose process validation requirements that slow but do not absolutely prohibit automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed in most jurisdictions, the task requires precise physical craftsmanship tied to patient-specific medical devices, creating quality and liability concerns that favor skilled human technicians. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Automated fabric-forming equipment is capital-intensive and requires specialized integration; the cost per task-equivalent remains comparable to or higher than skilled technician labor when accounting for equipment amortization, maintenance, and oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for this physical task, so any comparison is moot—human labor remains the only option and thus effectively cheaper than a nonexistent AI alternative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some pilot robotic systems exist for fabric handling in industrial settings, but they remain narrow in scope, require extensive setup per material type, and have high error rates on complex contours typical of medical appliances—no mature production systems perform this task autonomously at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical bending/forming of fabric or materials to structural contours; this remains a manual craft task performed by skilled technicians. |
Mix pigments to match patients' skin coloring, according to formulas, and apply mixtures to orthotic or prosthetic devices.
20CI 10–30 · exposure 13 · augmentation 38 · importance 4.1/5 · click for rater detail
Mix pigments to match patients' skin coloring, according to formulas, and apply mixtures to orthotic or prosthetic devices.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Orthotic and prosthetic manufacturing remains relatively small-scale and geographically distributed; adoption of advanced automation in this sector lags behind information and finance industries, with most operations still relying on skilled manual labor. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance/prosthetic manufacturing is a low-digitization, physical craft sector with minimal AI agent adoption for hands-on fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted color matching algorithms could suggest pigment formulas based on skin tone photos, reducing trial-and-error and speeding the mixing phase, though the human technician would still apply the pigments and make final adjustments for patient satisfaction. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could potentially assist with color-matching formula calculation or reference lookup, but the core mixing and application work sees little meaningful AI augmentation today. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze skin tone through image processing, the task requires precise manual application of pigments to 3D prosthetic devices in real-time, which demands dexterous robotic manipulation currently unavailable in production systems. The high-touch, custom-fit nature of prosthetics makes end-to-end automation with the 50% time-saving threshold unrealistic today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical craft task requiring manual mixing of pigments and application to physical devices with tactile and visual judgment; no current AI system can perform the physical manipulation involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some friction exists due to patient customization requirements and the need for human oversight of color matching accuracy, but no hard legal licensing barriers prevent automation of the pigment mixing and application steps themselves. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed like medicine, this requires specialized craft skill, direct patient fitting, and quality control tied to patient comfort and device function, creating practical barriers to automation via robotics or AI alone. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic systems capable of handling prosthetic devices with pigment application precision would require significant capital investment and integration costs that far exceed the loaded labor cost of a technician for this specialized task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based substitute performing this physical task, so AI cost per task-equivalent is effectively infinite/inapplicable compared to a technician's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed commercial product reliably performs both pigment matching and application to prosthetic devices at production scale. Color-matching algorithms exist, but physically applying pigments to irregular prosthetic surfaces remains a largely manual craft requiring human expertise and real-time adjustment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical pigment mixing and application to prosthetic/orthotic devices; this remains entirely a manual skilled craft process. |
Instruct patients in use of prosthetic or orthotic devices.
19CI 14–25 · exposure 20 · augmentation 50 · importance 4.0/5 · click for rater detail
Instruct patients in use of prosthetic or orthotic devices.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Medical device instruction remains a low-digitization, human-contact-intensive sector; while some clinics may pilot AI video guides, widespread production adoption for autonomous instruction is minimal and slow due to risk aversion. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance fitting is a highly physical, hands-on healthcare service sector with low AI adoption and minimal digitization of this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-generated instructional videos, animated device-use guides, and reminder systems can usefully support technician-led training, improving patient retention and compliance, though the human technician remains central to safe, individualized instruction. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by generating instructional materials, videos, or answering patient questions about device care and use, but cannot replace in-person guidance and adjustment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI systems can provide text or video instructions about device use, prosthetic/orthotic training requires real-time physical demonstration, adaptive feedback based on individual patient physiology and mobility limitations, and hands-on correction—capabilities current AI cannot reliably deliver in clinical settings without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Patient instruction involves hands-on fitting adjustments, physical demonstration, and reading patient response/comfort in real time, which current AI cannot perform end-to-end.rating rrrationale |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and liability barriers exist: patient safety standards, orthotic/prosthetic fitting regulations, and product liability require qualified human technicians to evaluate fit and instruct use; automation would shift legal liability and is not yet accepted by regulators or insurance. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Device fitting instruction often requires certified orthotist/prosthetist involvement for safety, proper use, and liability reasons, creating strong professional and regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Creating and maintaining high-quality, personalized AI instruction systems requires significant upfront costs, and patient safety still demands technician oversight; the all-in cost (including human validation) likely exceeds direct technician wages for routine instruction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since no AI system can perform the physical instruction and fitting component, there is no viable AI cost comparison; a human technician remains required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI-generated instructional content (videos, written guides) exists and is deployed, but production systems do not reliably teach patients to use complex devices; clinical evaluation still requires trained technicians to assess fit, comfort, and safe technique in person. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product provides physical patient instruction on wearing/using prosthetic or orthotic devices; this remains a research-stage or non-existent capability for physical-world guidance. |
Take patients' body or limb measurements for use in device construction.
18CI 11–25 · exposure 13 · augmentation 50 · importance 4.1/5 · click for rater detail
Take patients' body or limb measurements for use in device construction.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Medical device manufacturing remains a regulated, specialized sector with limited public evidence of widespread AI adoption for measurement tasks; most facilities continue traditional manual measurement workflows with slow digitization relative to tech-forward sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Medical appliance fabrication is a small, specialized, hands-on manufacturing sector with limited digitization and slow uptake of automated measurement technology. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | 3D body scanning and measurement software can assist technicians by automating landmark detection and dimensional calculations, reducing manual transcription error and speeding analysis, though the technician must validate and interpret results. |
| Augmentation potential | claude-sonnet-5 | 3/5 | 3D scanning and digital measurement tools can assist technicians by speeding data capture and improving precision, though the technician still performs the physical interaction with the patient. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While digital measurement tools (3D scanning, depth cameras) can capture body dimensions, the task requires direct patient contact, positioning assessment, and real-time judgment about measurement landmarks that current AI systems cannot reliably execute without substantial human supervision and correction. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical, hands-on measurement of a patient's body or limb with calipers, tape, or casting materials, which current AI systems cannot perform without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Medical device construction involves regulatory oversight (FDA, ISO 13485), and taking precise anthropometric measurements for medical purposes typically requires a licensed technician or direct clinical supervision; liability and accuracy accountability create strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Accurate measurement is clinically critical for device fit and function, typically requiring a trained/certified technician physically present with the patient, creating strong liability and human-contact barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized 3D scanning hardware and software integration costs are significant, and the human technician must still oversee, validate, and refine measurements, making the all-in cost comparable to or higher than direct human measurement. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | 3D scanning hardware and software can reduce time somewhat, but the equipment and integration costs plus need for skilled human handling keep costs comparable to or higher than manual measurement in most clinics. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some measurement capture devices exist (e.g., 3D body scanners), but they require expert setup, manual landmark identification, and human verification to meet the precision standards needed for medical device fitting; no fully autonomous measurement-to-device-ready workflow is in production use. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously takes physical patient measurements for orthotic/prosthetic device construction; some 3D scanning tools assist but require a human operator. |
Repair, modify, or maintain medical supportive devices, such as artificial limbs, braces, or surgical supports, according to specifications.
16CI 14–19 · exposure 16 · augmentation 25 · importance 4.3/5 · click for rater detail
Repair, modify, or maintain medical supportive devices, such as artificial limbs, braces, or surgical supports, according to specifications.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Medical device technician shops are typically small, specialized, local operations with low digital infrastructure adoption. The regulatory burden and hand-crafted nature of the work mean adoption of AI/robotic automation has been negligible to date. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance technician work is a small, physical, craft-based trade with low digitization and no meaningful AI/robotic adoption trend. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with documentation, CAD design suggestions, or inventory management, but the core physical work of repair and fitting offers limited augmentation value when the technician must anyway perform hands-on fitting and adjustment with the patient in real time. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design specifications, CAD modeling, or diagnostic documentation, but offers little direct help with the physical repair and fitting work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves hands-on physical manipulation, precise fitting to individual anatomy, and real-time adjustment based on patient feedback—core aspects that current AI cannot perform. While AI could assist in design or documentation, the repair, modification, and maintenance of prosthetics and orthotics require dexterous robot manipulation at a level not yet reliably deployed in production. |
| Task automatability | claude-sonnet-5 | 2/5 | This is a hands-on physical repair and fabrication task involving custom-fitted devices, requiring manual dexterity and tactile judgment that current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Medical device modification and repair carry regulatory oversight (FDA/similar), quality assurance requirements, and potential liability if a repaired device fails on a patient. Most jurisdictions require or strongly expect human certification and sign-off on medical appliance servicing, creating strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not always requiring a specific license depending on jurisdiction, medical device work often involves quality/safety specifications, liability for patient-fitting devices, and employer certification requirements that create friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized expertise, precision tooling, and custom-per-patient nature of this work mean human technician labor remains far cheaper than any AI system capable of executing even a portion of the task reliably and safely. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven automation for this physical task, so the human technician remains the only cost-effective option; any robotic attempt would be far more expensive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial AI or robotic system reliably performs the full scope of repair, modification, and maintenance of medical appliances independently. This task remains firmly in specialized human technician territory with only nascent research into robotic prosthetic assembly. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs repair/modification of prosthetics or braces autonomously in production; this remains a research-stage robotics problem at best. |
Service or repair machinery used in the fabrication of appliances.
14CI 7–21 · exposure 8 · augmentation 50 · importance 3.6/5 · click for rater detail
Service or repair machinery used in the fabrication of appliances.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Medical appliance manufacturing is relatively specialized and concentrated; automation adoption in maintenance is slow due to the need for customized solutions, equipment diversity, and certification requirements. Digitization of repair processes remains limited outside large facilities. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Manufacturing and equipment maintenance sectors show slower, more cautious AI adoption for physical repair tasks compared to information-based industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist technicians via diagnostic image analysis, predictive maintenance alerts, and access to repair documentation or knowledge bases, improving decision speed and reducing manual lookups. However, assistance is limited to guidance; the technician must execute the physical repair. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with diagnostic support, repair manuals, predictive maintenance alerts, and troubleshooting guidance, improving technician efficiency even though the physical repair itself is unaffected. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Machinery repair involves unpredictable physical diagnostics, hands-on troubleshooting, and real-time problem-solving in diverse equipment contexts. Current AI cannot reliably diagnose complex mechanical failures or execute repairs end-to-end; humans must interpret symptoms and execute physical interventions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical repair task requiring diagnosis, disassembly, and manual manipulation of mechanical equipment, which is far beyond current AI capabilities without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Machinery repair often requires licensing, certification, and manufacturer authorization to maintain warranties and ensure safety compliance. Liability for faulty repairs creates strong error-cost asymmetry, and customer preference for certified human technicians reinforces adoption barriers. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the same way as medical practice, safety and equipment liability concerns plus the physical nature of the work create meaningful organizational friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic systems capable of machinery servicing, combined with integration and oversight, exceeds the loaded wage of a skilled appliance technician. Current AI tooling remains too expensive for this task's context. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical repair work, so AI cost comparison is moot and the human remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform end-to-end machinery repair and service. While vision systems can identify some defects and diagnostic tools exist, full repair work—parts replacement, calibration, testing—requires human judgment and physical manipulation beyond current automation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously service or repair fabrication machinery for medical appliance manufacturing; this remains a physical technician task. |
Cover or pad metal or plastic structures or devices, using coverings such as rubber, leather, felt, plastic, or fiberglass.
14CI 10–19 · exposure 8 · augmentation 25 · importance 4.2/5 · click for rater detail
Cover or pad metal or plastic structures or devices, using coverings such as rubber, leather, felt, plastic, or fiberglass.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Medical appliance technician work is typically found in small specialized shops and hospitals with low digitization; adoption of robotic covering automation remains minimal and confined to high-volume standardized manufacturing, not the bespoke appliance context. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance technician work is a low-digitization, physical fabrication trade with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-assisted design tools or robotic staging could help plan coverage layouts or hold parts steady, but the actual hand-application of varied materials remains primarily a manual skill where augmentation gains are modest compared to full automation's infeasibility. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers little direct assistance for the physical covering/padding process itself, though design software or pattern-generation tools might marginally aid planning stages. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically control robotic arms to apply coverings, the task requires precise measurement, adaptation to irregular surfaces, and quality assessment that current systems struggle with in unstructured physical environments. Most of the work remains dependent on manual dexterity and real-time problem-solving. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual fabrication task requiring physical dexterity to cut, shape, and apply cover materials to custom orthotic/prosthetic devices; no AI system can perform this physical manipulation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Medical devices may have certification or quality-assurance sign-off requirements that mandate human inspection or approval, creating some friction; however, the task itself is not legally restricted to licensed professionals, allowing for partial automation or substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed at the point of this specific sub-task, it occurs within medical device fabrication where quality and fit standards, liability for patient-contact devices, and craftsmanship expectations create meaningful friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic systems capable of flexibly covering irregular medical devices would cost far more to acquire, integrate, and maintain than the human labor involved in hand-covering work, which is relatively low-wage and requires minimal tooling. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not comparable; a robotic solution would require expensive custom hardware exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform the full covering/padding task on medical appliance structures at production scale. Robotic automation exists in controlled factory settings but not as a general solution for the varied geometries and material combinations in this specialty domain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this physical covering/padding task; it remains a hands-on craft skill performed by technicians in workshops. |
Make orthotic or prosthetic devices, using materials such as thermoplastic and thermosetting materials, metal alloys and leather, and hand or power tools.
12CI 7–16 · exposure 8 · augmentation 50 · importance 4.4/5 · click for rater detail
Make orthotic or prosthetic devices, using materials such as thermoplastic and thermosetting materials, metal alloys and leather, and hand or power tools.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI-assisted design and some CAD-driven manufacturing is occurring in high-end facilities, but the sector remains heavily craft-based with limited digital integration and strong reliance on skilled technicians. Adoption remains slow and localized to larger organizations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Medical device manufacturing is a specialized, moderately digitized sector with growing use of CAD/3D printing but limited broad AI-driven automation of physical fabrication in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted CAD, design optimization, and automated cutting/shaping can meaningfully improve a technician's productivity in planning and material prep, but final fitting, adjustment, and quality control remain human-driven tasks requiring judgment and skill. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted CAD design, scanning, and 3D-printing software can help technicians design and plan devices more efficiently, though the physical construction still requires human skill. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with design and CAD preparation, the physical construction of orthotic/prosthetic devices requires precise hand-tool manipulation, material adaptation, and real-time adjustment based on fit and function—tasks that current robots and autonomous systems cannot perform reliably end-to-end. Significant human oversight and skilled hands-on work remain essential. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on fabrication task requiring physical manipulation of materials, molding, fitting, and use of hand/power tools—current AI systems cannot perform physical manufacturing.'}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Orthotic and prosthetic device fitting and fabrication often require state licensing and practitioner sign-off; liability for device failure is high; and regulatory bodies (FDA, state boards) require human accountability. These hard regulatory and liability barriers substantially protect the role. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Device fabrication for patient care often falls under medical device regulations and quality standards, and errors can cause patient harm, creating high liability and certification barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current partial automation (CAD, cutting) does not reduce overall labor cost below that of a skilled technician; integration, oversight, and hand-finishing still require the technician's wage. Full-system automation would be prohibitively expensive for the volumes and customization involved. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the physical fabrication, so there is no viable AI-only cost comparison; human technicians remain necessary for the physical work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems reliably perform the full fabrication of orthotic or prosthetic devices from raw materials using hand and power tools. Robotic solutions exist for narrow subtasks (e.g., cutting), but not for the integrated problem-solving, fitting, and adjustment required in this craft. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product fabricates custom orthotic/prosthetic devices from raw materials; some 3D-printing/CAD tools assist design but not the physical making process itself. |
Test medical supportive devices for proper alignment, movement, or biomechanical stability, using meters and alignment fixtures.
9CI 5–14 · exposure 8 · augmentation 25 · importance 4.2/5 · click for rater detail
Test medical supportive devices for proper alignment, movement, or biomechanical stability, using meters and alignment fixtures.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Medical device testing remains a low-digitization, heavily regulated domain where adoption of AI or robotic automation has been minimal. The small, specialized workforce and patient-safety-critical nature of the work limit pilot and production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance manufacturing is a physical, hands-on trade with low digitization and minimal AI/robotic adoption for this specific fitting and testing function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with data logging, measurement recording, or flagging anomalies in meter readings, but the core physical testing and expert judgment of biomechanical stability remain fundamentally human-dependent activities that AI tools have not substantially enhanced in practice. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital meters and sensor-based fixtures may already provide data readouts, and AI-enabled analytics could help interpret alignment data, but this offers only modest assistance to the core physical testing task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | The task requires physical manipulation of devices on a patient's body or limb, precise sensor readings, and nuanced judgment of biomechanical alignment—capabilities largely unavailable in current AI systems. While image-based measurement and simple alignment checks could be partially automated, the core tactile testing and stability assessment remain beyond current deployed automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires hands-on physical testing of physical devices (braces, prosthetics) with meters and alignment fixtures, involving tactile and visual assessment of fit on the human body - current AI has no embodied capability to perform this.dev |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Medical device testing falls under FDA and other regulatory oversight, often requiring a licensed or certified technician to perform and sign off on results. Patient safety liability and the requirement for human judgment in alignment assessment create substantial legal and compliance barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Medical device quality and safety testing often falls under regulatory and liability frameworks requiring qualified human verification, and improper alignment could cause patient harm, creating high error-cost asymmetry. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized equipment, measurement infrastructure, and human expertise required make this task expensive to automate; current robotic or AI solutions capable of biomechanical testing would cost far more than employing a trained technician. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical measurement task, so AI cost is not comparable to human labor cost for this specific task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs end-to-end biomechanical testing of medical devices. This task involves hardware fixtures, physical patient interaction, and specialized judgment that exists only in technician expertise, not in production AI systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products test physical medical appliances for alignment/biomechanical stability autonomously; this is a manual, hands-on quality-control task performed by technicians. |
Drill and tap holes for rivets, and glue, weld, bolt, or rivet parts together to form prosthetic or orthotic devices.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail
Drill and tap holes for rivets, and glue, weld, bolt, or rivet parts together to form prosthetic or orthotic devices.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Medical device manufacturing remains heavily labor-dependent and slow to adopt advanced robotics, particularly for custom or small-batch prosthetics and orthotics. The sector is fragmented among small and mid-sized fabrication shops with limited capital for automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance manufacturing is a small-scale, low-digitization physical trade with minimal AI/robotics adoption for custom fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance; basic computer vision or positioning aids might help with layout or measurement, but the core manual assembly operations—drilling, welding, riveting—are not meaningfully augmented by current AI tools; human expertise and tactile feedback remain dominant. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with CAD design or fit modeling upstream, but offers little direct assistance during the physical drilling, gluing, welding, and riveting steps themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three-dimensional space—drilling, tapping, welding, gluing, bolting, and riveting—involving custom-fit medical devices. Current AI and robotics lack the dexterity, sensorimotor feedback, and adaptive problem-solving needed to perform these varied assembly operations reliably on the irregular geometries and material combinations typical of prosthetics and orthotics. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication task requiring manual dexterity, precision drilling, welding, and assembly of custom-fitted devices; no off-the-shelf AI system can perform this hands-on manufacturing work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Medical devices are heavily regulated (FDA, ISO 13485); assembly is often part of the device manufacturing chain subject to quality assurance and traceability requirements. Liability and regulatory oversight for device assembly create substantial friction against full automation without human sign-off and accountability. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed for this specific step, custom medical device fabrication requires quality control tied to patient safety and fitting accuracy, creating organizational and liability-driven barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic systems capable of performing even a subset of these assembly operations would require significant capital investment, programming, and maintenance—vastly exceeding the labor cost of a skilled technician performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic solution for this custom, low-volume physical assembly work, so any automation would require expensive specialized robotics far costlier than a skilled technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs the full assembly sequence (drilling, tapping, gluing, welding, bolting, riveting) on custom prosthetic or orthotic devices in production. Specialized industrial robots exist for narrow, repetitive tasks but lack the flexibility and sensing required for the heterogeneous, patient-specific work described here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical drilling, welding, or riveting of prosthetic/orthotic components; this remains a manual technician task requiring robotic hardware, not AI software. |
Construct or receive casts or impressions of patients' torsos or limbs for use as cutting and fabrication patterns.
6CI 5–7 · exposure 0 · augmentation 38 · importance 4.3/5 · click for rater detail
Construct or receive casts or impressions of patients' torsos or limbs for use as cutting and fabrication patterns.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Medical device and prosthetics sectors are adopting digital scanning (3D photogrammetry, structured light) but the intake step of casting/impression from patients remains largely manual; adoption of fully autonomous systems is minimal and slow given physical and liability constraints. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance technician work is a highly manual, low-digitization trade with minimal AI adoption reported; the physical fabrication and fitting process has seen little AI-driven transformation to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design of patterns from digital scans, and computational refinement of traditional casting data, can improve downstream fabrication; however, the impression-taking stage itself offers limited augmentation since it remains tactile and patient-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with digital scanning-to-3D-model conversion or design optimization once an impression exists, but the core task of constructing/receiving the physical cast itself receives little to no AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires physical interaction with patients' bodies (taking impressions and casts) and precise 3D spatial manipulation that current AI cannot perform. While digital modeling of existing scans exists, the core work—receiving/constructing custom impressions from live patients—remains entirely physical and human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical hands-on interaction with patients to create or handle casts/impressions of body parts, a manual, tactile task that current AI systems cannot perform since they lack robotic embodiment for this specialized physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Direct patient contact and physical manipulation create inherent barriers; there is no regulatory requirement for a licensed person to approve the task, but the hands-on nature and need for patient comfort/safety create strong organizational and practical friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | While not always requiring formal licensure per se, this task demands direct physical patient contact, precise anatomical fitting, and quality control that has real consequences for patient comfort and device function, creating strong practical barriers to automation via non-physical AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The equipment, materials, and trained labor for casting patients remain human-centric. Digital scanning alternatives exist but still require technician oversight, and the all-in cost of any automated impression system would exceed the loaded wage of a technician performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is not comparable—the human technician remains the only viable option, making AI more expensive (effectively infinite) or simply inapplicable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously take body casts or impressions from patients. This requires embodied manipulation, tactile feedback, and real-time patient interaction that is beyond current robotic or AI capability in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical cast-taking or impression-molding of patient anatomy; this remains entirely a human manual craft task requiring physical presence and dexterity. |
Fit appliances onto patients, and make any necessary adjustments.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Fit appliances onto patients, and make any necessary adjustments.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Healthcare adoption of AI for physical patient interaction remains very limited; the sector is heavily regulated and conservative in delegating hands-on patient care tasks, and no significant production deployment of AI for appliance fitting is evident. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Medical appliance fabrication and fitting is a low-digitization, hands-on trade with minimal AI/robotic adoption in practice today. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with documentation, measurement recording, or appliance selection guidance, but the core fitting and adjustment work is inherently manual and tactile, limiting meaningful productivity assistance in the primary task itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI-assisted 3D scanning and CAD modeling can inform appliance design beforehand, but the actual physical fitting and adjustment on the patient receives little direct AI assistance. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Fitting appliances onto patients requires real-time physical manipulation, tactile feedback, and individualized adjustment based on patient anatomy and comfort—capabilities that current AI systems cannot perform. No end-to-end automation is feasible without robotics, which remains outside deployed general systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation, hands-on measurement, and real-time tactile adjustment of orthotic/prosthetic devices on a patient's body, which current AI systems and robotics cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Medical appliance fitting is typically regulated and requires licensure or certification; patient safety, liability, and legal requirements for human oversight or sign-off create hard barriers to full automation regardless of technical capability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Fitting medical appliances often requires certified technician oversight and direct patient contact, with liability concerns around improper fit causing injury or ineffective treatment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of this task do not exist at scale, making cost comparison impossible. The specialized robotics and sensing required would be extremely expensive compared to a trained technician's labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of substituting for this physical task, so any comparison favors the human technician entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical fitting and adjustment of medical appliances on human patients in production settings. This task requires embodied, real-time interaction that is not yet a mature, deployable capability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical fitting and adjustment of medical appliances on patients; this remains entirely a manual clinical skill. |
Related occupations — Production
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.