Medical Equipment Repairers

49-9062.00

Median wage $61,660/yr65,990 employed (US)Rank #565 of 923 scored · top 61% by substitution

Test, adjust, or repair biomedical or electromedical equipment.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution24

Exposure24

Augmentation52

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

20 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

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%26

panel mean rating 2.0/5 → substitution pressure 26/100

Technical feasibility todayw 20%19

panel mean rating 1.8/5 → substitution pressure 19/100

Cost vs. human wagew 15%22

panel mean rating 1.9/5 → substitution pressure 22/100

Adoption barriersw 20%inverted — strong barriers lower the score31

panel mean rating 3.8/5 (barrier strength) → substitution pressure 31/100

Sector adoption velocityw 10%21

panel mean rating 1.8/5 → substitution pressure 21/100

Task breakdown (20 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.

Make computations relating to load requirements of wiring or equipment, using algebraic expressions and standard formulas.

CI 65–79 · exposure 70 · augmentation 88 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Medical equipment repair and HVAC/electrical maintenance are capital-intensive sectors with high digitization of technical processes. Calculation tools and CAD-integrated software are standard in these fields, and adoption of automated computation is already mature and widespread.
Sector adoption velocity	claude-sonnet-5	2/5	Medical equipment repair is a relatively low-digitization, hands-on trade sector where AI tools for engineering calculations are used in pockets but not deeply integrated organization-wide.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI-powered calculation tools dramatically augment technicians by instantly providing accurate load requirements, wire sizing, and equipment specifications, allowing them to work faster and with fewer errors while retaining oversight of the overall system design and safety implications.
Augmentation potential	claude-sonnet-5	4/5	AI and computational tools substantially speed up and reduce errors in load computations, letting technicians focus on physical inspection and judgment rather than manual math.
Task automatability	claude-haiku-4-5-20251001	4/5	These computations are deterministic calculations based on well-defined algebraic expressions and standard formulas. AI systems can reliably perform electrical load calculations, wire sizing, and equipment specification computations from input parameters, meeting the time-saving threshold with minimal human oversight once the correct inputs are provided.
Task automatability	claude-sonnet-5	4/5	Load calculations using algebraic expressions and standard formulas are well-structured numeric tasks that AI tools (calculators, spreadsheets, LLMs with computation) can perform quickly given correct input parameters. The main human contribution is data-gathering and verification, not the arithmetic itself.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While professional licensure (PE stamps) or organizational oversight may be required for final sign-off on critical infrastructure, the computation step itself faces no legal barriers and is already widely automated in practice without explicit human reperformance.
Adoption barriers	claude-sonnet-5	2/5	There's no licensing requirement to perform a calculation itself, though technicians often must be certified to interpret and apply results correctly, creating a moderate oversight barrier.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	The cost of running a computational routine or formula-based AI inference is orders of magnitude cheaper than the loaded wage of a skilled technician performing manual calculations and lookups, even accounting for validation overhead.
Cost vs. human wage	claude-sonnet-5	4/5	Once formulas and inputs are standardized, computational tools cost far less than paying a technician's time to do the same calculations manually, though initial technician verification is still needed.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature software tools and embedded calculators within HVAC/electrical design platforms already perform these computations reliably in production. Specialized engineering software and even spreadsheet-based solutions with formula libraries are widely deployed, though domain-specific verification workflows still exist.
Technical feasibility today	claude-sonnet-5	3/5	Engineering calculation software and AI-assisted tools exist and are used for load calculations in electrical/biomedical contexts, but they are typically embedded in specialized CAD/engineering suites rather than generalized deployed products across all repair contexts.

Keep records of maintenance, repair, and required updates of equipment.

CI 62–75 · exposure 70 · augmentation 75 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Medical device and equipment manufacturers, hospitals, and service organizations are actively adopting automated maintenance management systems with AI components. This aligns with broader digital transformation in healthcare and industrial settings, with production-level deployment already underway in large hospital systems and repair chains.
Sector adoption velocity	claude-sonnet-5	3/5	Healthcare facilities management is a moderately digitizing sector; some hospital systems use automated asset tracking and CMMS but many still lag in full digitization of maintenance documentation.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly assists technicians by auto-generating preliminary logs from sensor data, photos, and work-order summaries, allowing them to review and approve rather than manually transcribe. This raises technician productivity while maintaining human oversight of record accuracy and regulatory compliance.
Augmentation potential	claude-sonnet-5	4/5	AI can substantially speed up documentation via automatic transcription, templated reports, and prompts for missing data while the technician retains oversight for accuracy and compliance.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can easily extract maintenance/repair data from completed work, generate update logs, and organize records into structured formats with minimal human intervention. Modern systems can parse work orders, photos, and service notes to populate and maintain equipment records, achieving substantial time savings on the clerical and organizational portions of this task.
Task automatability	claude-sonnet-5	4/5	Recordkeeping of maintenance events, timestamps, and update logs is a structured data-entry/documentation task that AI-enabled systems (voice-to-text, CMMS integrations, form auto-fill) can largely handle with human verification.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While record-keeping itself has no strict licensing barrier, healthcare and safety regulations (e.g., FDA, HIPAA for medical device repair records) may require auditable human review and sign-off on certain records. Integration friction and data security/compliance requirements create moderate adoption friction.
Adoption barriers	claude-sonnet-5	2/5	Regulatory requirements (e.g., FDA, Joint Commission) mandate accurate maintenance records for medical equipment, requiring human accountability and sign-off, but the act of recordkeeping itself isn't legally restricted to a licensed person.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI systems for record-keeping cost a fraction of manual data entry by technicians or administrative staff. Once integrated into existing systems, the per-task cost of automated logging and record organization is substantially lower than paying human labor for clerical work.
Cost vs. human wage	claude-sonnet-5	4/5	Automated logging via software integrations and speech-to-text is far cheaper than having skilled technicians spend time on manual paperwork, though some integration/setup cost remains.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (ERP systems with AI modules, IoT maintenance platforms, and document-processing tools) reliably handle equipment record-keeping at scale in manufacturing and healthcare settings. Integration with sensor data and automated work-order systems enables production-grade performance, though occasional validation of complex entries may be needed.
Technical feasibility today	claude-sonnet-5	3/5	CMMS and biomedical equipment management software with AI-assisted logging exist and are used in hospitals, but many facilities still rely on manual entry or semi-digitized processes, so reliability varies.

Research catalogs or repair part lists to locate sources for repair parts, requisitioning parts and recording their receipt.

CI 48–65 · exposure 58 · augmentation 88 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical device repair operates in slower-adopting sectors (healthcare, field service) with strong regulatory and quality oversight; digital parts lookup and requisition tools exist but penetration remains limited, with many shops still relying on manual catalog searches and phone orders.
Sector adoption velocity	claude-sonnet-5	2/5	Medical equipment repair and field service sectors are traditionally slower to adopt AI-driven procurement automation compared to information/finance sectors, though inventory management software adoption is growing.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can significantly augment technicians by rapidly cross-referencing multiple catalogs, suggesting equivalent parts, tracking receipt, and flagging discrepancies—substantially reducing the time spent on research and documentation while the technician retains decision authority on selections.
Augmentation potential	claude-sonnet-5	5/5	AI search and recommendation tools can dramatically speed up finding correct replacement parts and cross-referencing part numbers, significantly boosting technician productivity while they retain final judgment.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can assist with searching catalogs and part lists to identify sources and can automate requisition form population, but the task involves judgment about part compatibility, supplier selection, and integration with organizational systems that typically requires human oversight and verification.
Task automatability	claude-sonnet-5	4/5	Searching catalogs/parts lists and generating requisitions is largely structured information retrieval and data entry, which AI-augmented procurement tools can handle end-to-end with minimal human input for most cases.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While no strict licensing bars automation, organizational procurement policies, supplier relationships, quality control requirements, and the need for human authorization of orders create moderate friction against full substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks automation of parts sourcing, though organizational procurement policies and approval workflows create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI-assisted lookup and requisition systems cost roughly comparable to a technician's time spent on these research and administrative tasks when accounting for integration and ongoing oversight, with modest savings possible in high-volume environments.
Cost vs. human wage	claude-sonnet-5	4/5	Automated parts search and requisition systems are far cheaper to run than paying a technician's time for manual catalog research, though initial integration with inventory/ERP systems adds some cost.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products exist for parts lookup and inventory management, but they operate within specific ecosystems (manufacturer databases, ERP systems) and require human validation of part specifications and supplier selection; no fully autonomous end-to-end solution reliably handles the variability of equipment types and sourcing requirements.
Technical feasibility today	claude-sonnet-5	3/5	Procurement and parts-lookup software with AI-assisted search exists and is used in industry, but full automation of sourcing rare/legacy medical equipment parts still often requires human verification due to inconsistent catalogs and supplier data.

Study technical manuals or attend training sessions provided by equipment manufacturers to maintain current knowledge.

CI 30–48 · exposure 42 · augmentation 75 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical device service organizations are digitizing knowledge management slowly; while some use AI tools for document assist, the industry remains dominated by traditional manual study and manufacturer-led in-person training, reflecting conservative adoption patterns in highly regulated sectors.
Sector adoption velocity	claude-sonnet-5	2/5	Medical equipment repair is a specialized, lower-digitization trade sector where AI tool adoption for continuing education is still nascent compared to information/professional services sectors.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist technicians by rapidly extracting key information from manuals, generating summaries, answering specific technical questions, and creating flashcards or study guides, meaningfully raising the efficiency of professional development while keeping the human responsible for validation and integration.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully augment this task by quickly summarizing dense technical manuals, answering clarifying questions, and creating study aids, improving comprehension speed even though formal training remains human-attended.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist in summarizing or explaining technical manuals and generate study aids, but the task requires judgment about which knowledge to prioritize, contextual understanding of equipment variations, and professional accountability for staying current—functions that demand human decision-making and cannot be fully automated today.
Task automatability	claude-sonnet-5	3/5	AI can summarize manuals, generate study guides, and answer questions about technical documentation, saving significant time, but hands-on training sessions and physical familiarization with equipment still require human attendance.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Equipment manufacturers often control access to training sessions and certification pathways, and regulatory/compliance requirements frequently mandate that technicians maintain documented, verifiable knowledge directly tied to their professional credentials—these are structural barriers to full automation.
Adoption barriers	claude-sonnet-5	3/5	Some medical equipment requires manufacturer-certified training for warranty/service authorization and regulatory compliance, creating moderate friction against skipping formal training via AI substitutes.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI can reduce the time spent extracting and organizing information from manuals, but humans must still engage with content, attend manufacturer sessions, and validate learning, so total cost savings are modest and do not approach an order of magnitude difference.
Cost vs. human wage	claude-sonnet-5	3/5	AI-assisted document study is cheap relative to human time spent reading, but mandatory in-person or manufacturer-certified training sessions still incur similar or higher costs regardless of AI use.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	AI-powered document summarization and knowledge synthesis tools exist and are deployed, but they have material limitations in accuracy for specialized technical content and cannot independently attend training sessions or validate comprehension against real equipment performance.
Technical feasibility today	claude-sonnet-5	3/5	Deployed LLM tools reliably summarize and query technical documents today, but attending manufacturer training sessions and building tacit hands-on knowledge is not something products replace.

Evaluate technical specifications to identify equipment or systems best suited for intended use and possible purchase, based on specifications, user needs, or technical requirements.

CI 29–34 · exposure 25 · augmentation 63 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical device procurement remains a conservative, highly regulated space with slow digitization. Equipment repair shops and clinical engineering departments are not early adopters of autonomous AI systems; purchasing decisions involve multiple stakeholders and compliance checks that slow automation.
Sector adoption velocity	claude-sonnet-5	2/5	Healthcare technical/biomedical engineering functions have historically been slow to adopt AI-driven procurement tools compared to fast-moving digital sectors.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by organizing and highlighting relevant specification data, cross-referencing user requirements with equipment features, and flagging regulatory concerns—helping repairers work faster—but the final matching decision and validation remain with the experienced technician.
Augmentation potential	claude-sonnet-5	4/5	AI can efficiently digest technical specifications, compare datasheets, and flag key differences, substantially speeding up the research phase for a human decision-maker.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can parse and compare technical specifications, this task requires judgment about user needs and operational context that medical equipment repairers develop through experience. Current systems can assist in spec comparison but cannot reliably match equipment to the full range of clinical and institutional requirements without substantial human validation.
Task automatability	claude-sonnet-5	2/5	AI can help summarize specs and compare features, but final procurement judgments involve integrating clinical workflow needs, budget, vendor reliability, and regulatory compliance that require human domain expertise and organizational context.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical device selection often requires documented accountability and compliance with regulatory standards (FDA, institutional procurement rules). Equipment purchases involve liability considerations where a licensed/certified professional's sign-off is typically required or expected, creating legal and organizational friction against full automation.
Adoption barriers	claude-sonnet-5	3/5	No strict licensing requirement for equipment selection itself, but liability for wrong equipment choices in patient-care settings creates meaningful organizational caution and review requirements.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI-assisted specification analysis would be modestly cheaper than manual review, but the task includes judgment steps (user needs assessment, suitability evaluation) that still require expert oversight, making the total cost comparable to hiring experienced staff.
Cost vs. human wage	claude-sonnet-5	3/5	AI can cheaply process technical documents, but human oversight, verification against clinical/regulatory needs, and vendor negotiation still require significant skilled labor, keeping costs roughly comparable.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed products reliably perform equipment selection for medical contexts independently. Spec databases and comparison tools exist, but medical equipment selection involves regulatory compliance, clinical workflow integration, and safety considerations that require human expertise to evaluate properly.
Technical feasibility today	claude-sonnet-5	2/5	Products like LLM-based comparison tools or spec-summarization assistants exist but are not deployed specifically for medical equipment procurement decisions with reliability at scale.

Explain or demonstrate correct operation or preventive maintenance of medical equipment to personnel.

CI 25–30 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Healthcare adoption of AI for training and demonstration remains limited; most medical equipment instruction relies on manufacturer-provided human trainers and institutional training programs. Adoption is slower than in information/finance sectors due to safety, regulatory, and quality assurance constraints.
Sector adoption velocity	claude-sonnet-5	2/5	Medical equipment repair is a physical, hands-on trade with low digitization and slow AI adoption compared to information-sector jobs.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist trainers by generating initial instructional scripts, creating supplementary written or video materials, or tracking trainee comprehension—useful but not transformative. The human trainer remains essential for adaptive explanation, real-time troubleshooting, and hands-on demonstration.
Augmentation potential	claude-sonnet-5	4/5	AI can significantly aid in creating training scripts, quick-reference guides, video explanations, and answering procedural questions, enhancing the human trainer's efficiency.
Task automatability	claude-haiku-4-5-20251001	2/5	Explaining and demonstrating equipment operation requires real-time assessment of personnel understanding, personalized instruction adapted to learner questions, and hands-on demonstration—tasks where current AI has limited capability. While AI can generate pre-recorded explanations or written manuals, the interactive, responsive, and demonstrative aspects remain largely manual.
Task automatability	claude-sonnet-5	2/5	AI can generate training materials or explanations, but hands-on demonstration of physical equipment operation and real-time troubleshooting requires physical presence and manipulation that current AI cannot perform.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Healthcare settings typically require personnel certification, liability concerns around incorrect equipment operation, and regulatory expectations (e.g., FDA guidance on equipment training). Hospitals and clinics prefer direct human instruction and sign-off for patient safety reasons, creating organizational and compliance friction.
Adoption barriers	claude-sonnet-5	3/5	Medical equipment training often has compliance and liability requirements (e.g., ensuring staff are properly certified to use equipment), creating moderate friction against pure AI substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI solutions for generating instructional content or video-based demonstrations carry non-trivial setup and content creation costs, and still require human oversight and customization for medical equipment contexts. The all-in cost approaches or exceeds hiring a qualified trainer for routine instruction.
Cost vs. human wage	claude-sonnet-5	2/5	While AI-generated documentation is cheap, the physical demonstration component still requires a human technician on-site, limiting overall cost savings.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some AI products can generate instructional content or simulate equipment operation in controlled scenarios, but no deployed systems reliably perform live explanation and adaptive demonstration of medical equipment at the quality expected for safety-critical contexts. Products exist at research or pilot stage only.
Technical feasibility today	claude-sonnet-5	2/5	Chatbots and documentation tools exist to explain procedures, but no deployed product reliably demonstrates physical equipment operation or performs hands-on training in production settings.

Contribute expertise to develop medical maintenance standard operating procedures.

CI 25–25 · exposure 25 · augmentation 50 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Healthcare and medical device sectors adopt AI cautiously due to regulatory and safety constraints. SOP development remains a human-expert function in most organizations; adoption of AI-generated medical procedures is still rare and heavily supervised.
Sector adoption velocity	claude-sonnet-5	2/5	Healthcare equipment maintenance is a physically-grounded, highly regulated niche field with slow AI adoption relative to information-sector norms.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully assist repairers by drafting template sections, organizing regulatory requirements, and identifying potential gaps in procedure outlines, raising the speed of initial documentation work while the human expert retains full responsibility for safety and compliance.
Augmentation potential	claude-sonnet-5	3/5	AI can help organize, draft, and cross-reference regulatory language and best practices, giving moderate productivity gains while the expert retains full ownership of technical content.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can draft procedural documents and synthesize information, developing SOPs requires deep domain expertise, regulatory knowledge, and organizational context that medical equipment repairers accumulate through hands-on experience. AI cannot reliably generate the nuanced safety and compliance details that would meet the <50% time-saving bar without substantial expert oversight.
Task automatability	claude-sonnet-5	2/5	Drafting portions of SOPs from templates or regulatory text can be AI-assisted, but the core task requires expert judgment about specific equipment failure modes, safety-critical procedures, and hands-on repair experience that AI cannot originate.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical device regulations (FDA, ISO standards) and liability frameworks require that SOPs be developed and validated by qualified human experts; organizations face legal and safety accountability for procedure quality, creating strong organizational and regulatory friction against full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical device maintenance SOPs are subject to regulatory compliance (e.g., FDA, Joint Commission) and often require sign-off by qualified biomedical technicians, creating substantial liability and authorization barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The cost of AI systems plus the extensive human expert review and revision required to ensure safety and compliance approaches or exceeds the cost of having skilled repairers write SOPs directly.
Cost vs. human wage	claude-sonnet-5	2/5	AI drafting assistance is cheap, but the expert input, validation, and liability review still require the technician's specialized knowledge, so total cost savings are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably generates production-ready medical maintenance SOPs independently. LLMs can assist with drafting, but the liability and regulatory stakes mean human repairers must substantially rewrite and validate output, making end-to-end AI performance unreliable.
Technical feasibility today	claude-sonnet-5	2/5	General-purpose LLM tools can help draft or format documents, but no deployed product independently generates validated medical equipment maintenance SOPs used in production without heavy expert review.

Test, evaluate, and classify excess or in-use medical equipment and determine serviceability, condition, and disposition, in accordance with regulations.

CI 20–25 · exposure 20 · augmentation 50 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Healthcare maintenance and repair sectors are slower to adopt AI-driven automation due to regulatory constraints, safety criticality, and the distributed nature of equipment servicing across many facilities. Adoption remains primarily at the pilot stage.
Sector adoption velocity	claude-sonnet-5	2/5	Healthcare equipment maintenance is a physical, safety-regulated field with historically low AI adoption for hands-on repair and testing tasks, though software-assisted diagnostics are slowly emerging.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist technicians by automating image-based condition assessment, pulling relevant equipment specifications, and flagging regulatory considerations, improving efficiency in documentation and initial evaluation stages.
Augmentation potential	claude-sonnet-5	3/5	AI can assist with maintaining equipment logs, flagging anomalies from diagnostic data, and referencing regulatory documentation, improving efficiency of the classification and documentation portions of the task.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could assist in evaluating documentation and classifying equipment via image analysis, the task requires hands-on testing, physical inspection, and regulatory judgment that cannot be fully automated today. Current systems cannot reliably perform the full end-to-end workflow of testing, evaluating, and determining regulatory compliance disposition without significant human oversight.
Task automatability	claude-sonnet-5	2/5	This requires physical hands-on testing, diagnostics with specialized tools, and judgment calls about safety-critical equipment, which current AI cannot perform end-to-end without robotic embodiment and physical access.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Regulatory requirements and liability exposure are substantial: medical equipment serviceability determinations directly impact patient safety and typically require sign-off by qualified technicians per FDA and institutional regulations. This creates a hard barrier to full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment servicing is subject to regulatory compliance (e.g., FDA, biomedical safety standards) and often requires certified technicians to sign off on serviceability determinations, creating significant liability and licensing barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI systems capable of partial assistance (imaging, documentation review) still require expert technician time for testing and final determination, making the combined cost comparable to or higher than direct human labor for the complete task.
Cost vs. human wage	claude-sonnet-5	2/5	AI could assist with documentation and diagnostic data analysis cheaply, but the core physical testing and inspection still requires a human technician, keeping overall costs comparable to human labor.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed products reliably perform the full scope of this task (hands-on testing, evaluation, classification, and regulatory determination) independently. Some computer vision and documentation systems exist, but they operate only on narrow subsets and require substantial expert review.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product autonomously tests and classifies medical equipment physically; this remains a manual, hands-on inspection task performed by certified technicians.

Test or calibrate components or equipment, following manufacturers' manuals and troubleshooting techniques, using hand tools, power tools, or measuring devices.

CI 16–25 · exposure 20 · augmentation 50 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical device repair operates in a regulated, conservative sector with small distributed technician bases; adoption of automation remains minimal, with pilots rare and production deployment of autonomous repair systems virtually nonexistent.
Sector adoption velocity	claude-sonnet-5	2/5	Biomedical equipment repair is a physically-oriented, specialized trade with low current AI integration; adoption of AI tools in this niche sector remains nascent.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can provide real-time diagnostic guidance, calibration parameter lookup, and measurement interpretation to assist technicians, improving efficiency and accuracy on parts of the workflow while they perform the hands-on assembly and adjustment.
Augmentation potential	claude-sonnet-5	3/5	AI can assist by providing troubleshooting guidance, retrieving manufacturer manuals, interpreting error codes, and logging calibration data, improving technician efficiency without replacing physical actions.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can guide troubleshooting workflows and analyze measurement data, the task fundamentally requires hands-on physical manipulation of equipment with hand and power tools, real-time sensory feedback, and adaptive problem-solving in variable hardware contexts that current robots cannot reliably perform end-to-end.
Task automatability	claude-sonnet-5	2/5	This requires physical manipulation, hands-on testing with tools, and physical calibration of equipment which current AI cannot perform without embodiment; only diagnostic reasoning support is feasible.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical device regulations (FDA, etc.) typically require certified technicians to perform and sign off on calibration and testing; liability for faulty medical equipment repair creates strong legal and compliance barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment calibration often requires certified technicians due to patient safety, regulatory compliance (FDA, biomedical engineering standards), and liability concerns, creating strong barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current robotic systems capable of precise calibration and tool manipulation are very expensive relative to skilled technician labor, and integration costs for equipment-specific adaptation make substitution economically unfavorable.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the physical labor involved, so there is no comparable AI-only cost basis; human labor remains necessary and thus cheaper than any hypothetical robotic alternative.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some AI-assisted troubleshooting software and diagnostic systems exist, but no deployed product reliably performs the full cycle of testing, calibration, and hands-on adjustment at production scale; human technicians remain essential for the physical work.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously tests or calibrates medical equipment hardware today; this remains a physical, hands-on task performed by technicians.

Inspect, test, or troubleshoot malfunctioning medical or related equipment, following manufacturers' specifications and using test and analysis instruments.

CI 16–25 · exposure 20 · augmentation 50 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical equipment service remains concentrated in certified technician and manufacturer service networks with slower digital transformation than IT or finance sectors. Adoption of AI-assisted diagnostics is emerging but limited by regulatory caution, safety liability, and the specialized nature of the workforce.
Sector adoption velocity	claude-sonnet-5	2/5	Biomedical equipment repair is a physical, hands-on trade with low digitization and slow AI adoption compared to information-based professions.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can provide useful assistance through automated test sequence recommendation, historical fault database retrieval, and pattern analysis of sensor data, helping technicians work faster. However, augmentation is limited to decision support; the core inspection and hands-on testing remain human-driven.
Augmentation potential	claude-sonnet-5	3/5	AI-based diagnostic software, predictive maintenance analytics, and knowledge-base lookup tools can assist technicians in troubleshooting and interpreting error codes, improving efficiency without replacing hands-on work.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with diagnostic decision-making and pattern recognition in equipment logs, the physical inspection, hands-on testing with specialized instruments, and real-time troubleshooting of complex medical equipment require manual dexterity and contextual judgment that current AI systems cannot execute end-to-end. The task involves tactile diagnostics and safety-critical verification that remain primarily human work.
Task automatability	claude-sonnet-5	2/5	Physical inspection, hands-on testing with specialized instruments, and hardware troubleshooting require manipulation and sensing that current AI cannot perform autonomously; only diagnostic data analysis portions could be assisted.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical equipment repair is heavily regulated; technicians must often be certified or trained to manufacturers' standards, and liability for equipment failures falls on authorized service providers. Regulatory frameworks, warranty and liability requirements, and the medical safety context create substantial barriers to fully autonomous automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment repair often requires certification, manufacturer authorization, and compliance with regulatory/safety standards (e.g., FDA-related biomedical equipment rules), creating strong barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integration of AI diagnostic tools into repair workflows requires significant setup and human oversight, and the cost of false negatives in medical equipment is very high. Current AI-assisted solutions do not yet approach an order of magnitude cost reduction versus experienced technician labor for this safety-critical task.
Cost vs. human wage	claude-sonnet-5	1/5	Physical inspection and repair still require a human technician on-site with specialized tools, so AI cannot substitute cheaply for the physical labor involved.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some diagnostic support tools exist (e.g., automated test sequence generators, ML-based fault prediction from logs), but no deployed AI system reliably performs the complete inspection-test-troubleshoot cycle independently. Products assist human technicians but do not replace the core hands-on diagnostic role, and medical equipment safety requirements limit autonomous deployment.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously inspects or physically troubleshoots medical equipment; this remains a hands-on technician task with AI limited to research-stage diagnostic aids.

Plan and carry out work assignments, using blueprints, schematic drawings, technical manuals, wiring diagrams, or liquid or air flow sheets, following prescribed regulations, directives, or other instructions as required.

CI 16–25 · exposure 20 · augmentation 50 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical device repair remains a specialized, regulated field with limited digital transformation momentum. Most medical equipment servicing relies on manufacturer-trained technicians and legacy service protocols, with slow adoption of AI-assisted planning relative to other professional services.
Sector adoption velocity	claude-sonnet-5	2/5	Healthcare equipment maintenance is a physically-oriented, moderately regulated field with slow AI adoption for hands-on repair tasks, though diagnostic software aids are emerging slowly.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist technicians by automatically extracting and summarizing schematic information, flagging regulatory requirements, and suggesting work sequence optimizations, improving planning efficiency without replacing human judgment on complex troubleshooting and compliance decisions.
Augmentation potential	claude-sonnet-5	3/5	AI can help technicians quickly search manuals, interpret schematics, or troubleshoot via natural language queries, improving planning efficiency even though physical execution remains manual.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with interpreting technical documentation and generating work plans, the task requires physical execution on specialized equipment, real-time problem-solving during repair, and verification of complex systems that demand hands-on troubleshooting. Current AI systems cannot autonomously perform the repair work itself, only pre-plan portions of it.
Task automatability	claude-sonnet-5	2/5	This involves physical inspection, hands-on diagnosis, and repair of medical equipment guided by technical documents, which current AI cannot perform end-to-end; only the interpretive/planning portion of reading schematics could be assisted.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical device repair is heavily regulated (FDA, ISO 13485); technicians must often be certified, authorized, and personally accountable for compliance and safety. Liability for equipment failure, patient safety implications, and regulatory requirements that mandate human expertise and sign-off create substantial barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment repair is subject to regulatory compliance (e.g., FDA, biomedical engineering standards) and often requires certified technicians, creating strong liability and licensing barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The cost of integrating specialized AI systems to parse medical equipment schematics, maintain regulatory compliance, and generate accurate work plans, combined with necessary human oversight, remains comparable to or potentially exceeds the cost of experienced technician planning time.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the physical repair labor, so any cost comparison favors the human technician who must still perform hands-on work regardless of AI assistance.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some commercial tools can parse technical drawings and schematic diagrams to suggest procedural steps, but deployed products lack reliable capability to autonomously plan complete repair sequences for diverse medical equipment while ensuring compliance with medical device regulations and safety standards.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously plans and executes physical repair work assignments on medical equipment using technical manuals; this remains a human-performed physical and cognitive task.

Compute power and space requirements for installing medical, dental, or related equipment and install units to manufacturers' specifications.

CI 16–25 · exposure 20 · augmentation 50 · importance 3.7/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical device installation occurs in specialized, regulated healthcare and dental settings where adoption of autonomous AI remains minimal; adoption is slower than in less regulated technical fields due to compliance and liability concerns.
Sector adoption velocity	claude-sonnet-5	2/5	Equipment repair and installation trades show low AI adoption due to physical, hands-on nature and slower digitization in this sector.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist with calculation of power loads and space layouts from manufacturer specifications and facility blueprints, allowing technicians to verify and adjust requirements faster than manual computation, though human judgment remains essential for final installation decisions.
Augmentation potential	claude-sonnet-5	3/5	AI/software tools can assist with calculating power and space requirements, generating specs, and referencing manufacturer documentation, aiding the planning portion of the task.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with calculations of power and space requirements using manufacturer specifications and building data, the task requires on-site physical assessment, adaptation to actual installation constraints, and judgment about regulatory compliance that current systems cannot reliably execute end-to-end without human oversight.
Task automatability	claude-sonnet-5	2/5	Calculations for power/space requirements could be partially assisted by software, but physical installation to manufacturer specs requires hands-on work AI cannot perform, so end-to-end automation is far below the 50% threshold.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical equipment installation typically requires certified technician sign-off, manufacturer warranty compliance, and regulatory approval (FDA, electrical codes, safety standards), creating substantial legal and licensing barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment installation often requires certified technicians per manufacturer and regulatory (e.g., FDA, electrical/safety code) compliance, creating strong barriers to non-human substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI-assisted computation of power and space needs is inexpensive, but the expert labor required for actual installation, compliance verification, and troubleshooting remains the dominant cost, making overall replacement cost unfavorable compared to human technicians.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the physical labor and precision installation involved, so there is no viable AI cost comparison for the full task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed AI systems currently perform the full installation task independently; existing tools may help with requirement calculations from documentation, but physical installation and on-site problem-solving remain human-dependent in production environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product installs medical/dental equipment or performs the physical siting and hookup work; this remains a manual technician task.

Examine medical equipment or facility's structural environment and check for proper use of equipment to protect patients and staff from electrical or mechanical hazards and to ensure compliance with safety regulations.

CI 0–25 · exposure 13 · augmentation 50 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Medical equipment repair is a conservative, heavily regulated sector with strong emphasis on human expertise and accountability. Adoption of AI for autonomous safety inspection is slow; most facilities still rely on manual inspections and certified technicians.
Sector adoption velocity	claude-sonnet-5	1/5	Healthcare facilities maintenance and biomedical equipment servicing is a physically-grounded, low-digitization sector with minimal AI agent deployment for hands-on inspection tasks.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI vision tools can usefully assist technicians by pre-flagging potential hazards, documenting defects photographically, and organizing inspection checklists, thereby accelerating the inspection workflow while the human retains judgment and certification responsibility.
Augmentation potential	claude-sonnet-5	3/5	AI can assist by analyzing sensor logs, predicting failure risks, or maintaining digital compliance checklists, but the core physical inspection and hazard assessment still requires human presence and judgment.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI vision systems can detect some visible electrical hazards and physical damage patterns, this task requires nuanced judgment about structural safety, regulatory compliance interpretation, and risk assessment that current systems handle poorly. The integration of facility-specific context, equipment manuals, and dynamic safety standards makes end-to-end automation fall short of the 50% time-saving bar.
Task automatability	claude-sonnet-5	1/5	This requires hands-on physical inspection of equipment and facility environments, sensor testing, and judgment about mechanical/electrical hazards that current AI cannot perform end-to-end without a human physically present.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical safety inspections carry strong regulatory requirements (OSHA, facility licensing, JCE accreditation), and errors can trigger liability claims or patient harm. Many jurisdictions require human certification and sign-off on facility safety compliance, creating a hard barrier to full automation.
Adoption barriers	claude-sonnet-5	5/5	Regulatory compliance (e.g., Joint Commission, FDA, OSHA) mandates certified biomedical equipment technicians to inspect and sign off on safety, creating strong legal and liability barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI visual inspection systems and oversight infrastructure remain moderately expensive to deploy and maintain per facility, while the specialized expertise and liability assumption of a trained medical equipment repair technician is relatively well-established. Cost parity may exist in specific high-volume scenarios, but not consistently.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the physical inspection labor, so there is no viable cost comparison—human technicians remain the only option for the physical task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Deployed computer vision products can flag obvious defects (frayed cables, loose connections), but production systems do not reliably assess facility-wide structural and electrical safety, regulatory compliance, or contextual risk in real healthcare environments. Most real deployments remain pilots rather than scaled production systems.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously performs physical safety inspections of medical equipment and facility electrical/mechanical hazards; this remains a manual, certified technician task.

Repair shop equipment, metal furniture, or hospital equipment, including welding broken parts or replacing missing parts, or bring item into local shop for major repairs.

CI 5–19 · exposure 13 · augmentation 25 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Medical equipment repair shops are typically small, dispersed, low-digitization operations with bespoke equipment; adoption of automation is minimal and hampered by low volume and high variability per shop.
Sector adoption velocity	claude-sonnet-5	1/5	Equipment repair trades are physical, hands-on work with low digitization and minimal AI/robotic adoption in production settings.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI can assist with parts identification, troubleshooting guides, and documentation, but offers limited productivity gain for the physically intensive core tasks of welding, assembly, and hands-on repair that dominate the work.
Augmentation potential	claude-sonnet-5	2/5	AI could assist with diagnostic troubleshooting, documentation, or parts lookup, but offers little help with the core hands-on welding and repair work.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with diagnostics and parts ordering, the core task requires physical manipulation (welding, part replacement) that current robotics cannot reliably perform on heterogeneous hospital equipment in unstructured shop environments without extensive custom setup.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation, welding, diagnosis of mechanical/electrical faults, and part replacement on diverse equipment—none of which current AI systems can perform end-to-end without robotics far beyond deployed capability.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Repair of hospital equipment often involves FDA-regulated medical devices where modifications require documentation, validation, and sometimes certification; liability for failed repairs is substantial, creating legal barriers to full automation without qualified human sign-off.
Adoption barriers	claude-sonnet-5	4/5	Hospital equipment often requires certified technicians for safety/regulatory compliance, and liability for faulty repairs on medical devices is high, creating strong barriers to non-human or unverified automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of industrial robots capable of welding and precision part replacement, plus ongoing integration and oversight, exceeds the loaded wage of a skilled repair technician for highly variable, low-volume repair jobs.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing the physical repair work, so AI cost is irrelevant/infinite relative to a human technician who can actually complete the job.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No mature product suite handles end-to-end repair of diverse medical equipment including welding and assembly. Some robotic systems exist for structured tasks, but none demonstrate reliable performance across the variety of equipment types and damage patterns a repair technician encounters.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously welds, repairs, or replaces parts on hospital equipment or metal furniture; this remains firmly in the physical/manual domain unaddressed by AI products.

Disassemble malfunctioning equipment and remove, repair, or replace defective parts, such as motors, clutches, or transformers.

CI 5–16 · exposure 8 · augmentation 50 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption of automation in medical equipment repair has been slow, with most organizations still relying on skilled human technicians; robotic solutions remain niche and limited to large facilities with standardized equipment.
Sector adoption velocity	claude-sonnet-5	1/5	Physical equipment repair in healthcare facilities is a low-digitization, hands-on trade with minimal AI/robotic adoption for actual repair execution.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist with diagnostics, parts identification, and access to repair manuals through knowledge systems, raising a technician's speed and accuracy in some workflow stages, though the hands-on repair work remains human-driven.
Augmentation potential	claude-sonnet-5	3/5	AI can assist with diagnostics, fault prediction, repair manuals, and troubleshooting guidance, but the physical repair steps themselves see little augmentation beyond information support.
Task automatability	claude-haiku-4-5-20251001	2/5	While some diagnostic and parts-identification steps could be partially automated, the physical disassembly, repair, and reassembly of mechanical/electrical equipment require dexterity, spatial reasoning, and real-time problem-solving that current AI systems cannot reliably perform end-to-end with 50% time savings.
Task automatability	claude-sonnet-5	1/5	This is a hands-on physical disassembly, diagnosis, and repair task requiring fine motor manipulation and tactile judgment that current AI cannot perform end-to-end without robotic embodiment far beyond available systems.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical equipment repair is subject to strict FDA and safety regulations; liability for incorrect repair is high; and technicians often require certification or licensure. These regulatory and liability barriers significantly protect the human role.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment repair often involves regulatory compliance (FDA, biomedical safety standards), liability for patient safety, and frequently requires certified technicians, creating strong barriers to non-human substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic systems capable of performing mechanical disassembly and repair would require significant capital investment, ongoing maintenance, and specialized integration—far exceeding the loaded wage of a skilled medical equipment repairer.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical repair work, so any AI-based approach would require robotics infrastructure vastly more costly than a technician's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs physical disassembly and repair of medical equipment autonomously; this remains a task requiring human technicians with domain knowledge and manual skill, though diagnostic imaging and documentation tools exist.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously disassembles medical equipment and replaces defective mechanical/electrical parts; this remains firmly in the domain of skilled human technicians.

Fabricate, dress down, or substitute parts or major new items to modify equipment to meet unique operational or research needs, working from job orders, sketches, modification orders, samples, or discussions with operating officials.

CI 5–14 · exposure 8 · augmentation 25 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Medical device repair and modification occurs in regulated, capital-equipment-focused sectors with high safety requirements and low digitization of the core fabrication process. Adoption of autonomous AI systems in this domain is minimal and moving slowly.
Sector adoption velocity	claude-sonnet-5	1/5	Equipment repair and fabrication is a physical, hands-on trade with low digitization and minimal AI/robotics adoption in production settings.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could help with design documentation, CAD optimization, or searching technical specifications for compatible parts, but the core task of physical fabrication and hands-on modification offers limited augmentation potential with current systems.
Augmentation potential	claude-sonnet-5	2/5	AI could help interpret sketches, generate CAD models, or suggest part specifications, but it offers limited assistance for the core physical fabrication and fitting work.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could assist in design optimization and documentation review, the task requires hands-on fabrication, physical assembly, and real-time adaptation based on unique operational constraints. Current AI cannot independently perform the mechanical work, although it may help with planning and modification orders.
Task automatability	claude-sonnet-5	1/5	This requires physical fabrication, machining, and hands-on custom part-making informed by ambiguous client input—well outside current AI capability without robotics that don't exist for this niche.09
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical equipment modifications have significant regulatory oversight (FDA compliance, safety certification), liability concerns if modifications fail, and often require documented sign-off by qualified technicians. These legal and safety barriers meaningfully protect the task from automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment modifications often require compliance with safety regulations, manufacturer specifications, and biomedical engineering oversight, creating strong liability and regulatory barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI systems capable of any part of this task would require specialized robotics and custom integration far more expensive than the loaded cost of a skilled medical equipment technician. The per-task cost would be prohibitively high compared to human labor.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing the physical fabrication and fitting work, so the human remains the only cost-effective option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product can autonomously fabricate, dress down, or substitute physical parts on medical equipment. This task requires embodied robotics, domain-specific machining knowledge, and physical problem-solving in a safety-critical context where mature, production-scale systems do not exist.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product fabricates or physically modifies medical equipment parts based on sketches and discussions; this remains a manual technician task.

Perform preventive maintenance or service, such as cleaning, lubricating, or adjusting equipment.

CI 0–16 · exposure 8 · augmentation 38 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Healthcare is digitized in information systems but maintenance remains mostly manual and labor-intensive; adoption of automated or AI-driven physical maintenance is in early pilot stages at best, with most preventive maintenance still performed by human technicians following schedules and procedures.
Sector adoption velocity	claude-sonnet-5	1/5	This is a physical, hands-on trade with low digitization; the broader field of physical equipment repair shows minimal AI-driven displacement or adoption of autonomous systems.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist by predicting maintenance needs through sensor data analysis and condition monitoring, or by guiding technicians through service procedures via augmented reality or decision support, but the core physical execution remains human-dependent.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with diagnostic support, maintenance scheduling, or documentation, but offers little direct help with the physical acts of cleaning, lubricating, or adjusting equipment.
Task automatability	claude-haiku-4-5-20251001	2/5	Preventive maintenance involves physical manipulation (cleaning, lubricating, adjusting) that current AI systems cannot perform autonomously in real environments. While diagnostic assessment of maintenance needs could be partially automated, the hands-on execution remains fundamentally dependent on human technicians.
Task automatability	claude-sonnet-5	1/5	Physical hands-on tasks like cleaning, lubricating, and adjusting medical equipment require manual dexterity and physical presence that current AI systems cannot provide; AI cannot manipulate physical hardware autonomously.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Medical device maintenance is heavily regulated (FDA, ISO 13485); technicians typically require certification and licensing, and liability for improper maintenance falls on authorized service personnel. Regulatory requirements and warranty obligations create hard barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment maintenance often requires certified technicians due to patient safety regulations, liability concerns, and manufacturer service requirements, creating substantial barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Medical equipment maintenance requires physical presence and precision work that robotic systems capable of performing it remain extremely expensive compared to trained technician labor; AI systems that could theoretically assist would add cost rather than reduce it meaningfully.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for the physical labor involved, so cost comparison favors human technicians entirely; any robotic solution would be far more expensive than a technician's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI products perform end-to-end preventive maintenance of medical equipment. Diagnostic AI exists but cannot physically execute cleaning, lubrication, or mechanical adjustment without specialized robotics that are not yet reliably deployed at scale in maintenance contexts.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform physical preventive maintenance on medical equipment; robotics for this specific application remains research-stage or nonexistent in production.

Supervise or advise subordinate personnel.

CI 0–16 · exposure 8 · augmentation 50 · importance 3.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	This task involves human judgment, accountability, and organizational authority that organizations are highly reluctant to cede to AI; adoption remains negligible.
Sector adoption velocity	claude-sonnet-5	2/5	Medical equipment repair is a physical, hands-on trade with low digitization and slow AI adoption for management functions specifically.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist supervisors by analyzing performance data, suggesting coaching points, or flagging issues requiring attention, but the human supervisor remains responsible for final decisions and personnel guidance.
Augmentation potential	claude-sonnet-5	3/5	AI tools can assist supervisors with scheduling, performance tracking, training material generation, and knowledge retrieval, providing moderate productivity support while the human retains the supervisory role.
Task automatability	claude-haiku-4-5-20251001	2/5	Supervising or advising subordinates requires real-time judgment, interpersonal dynamics, and contextual awareness that current AI cannot reliably replicate end-to-end. While AI can assist with performance tracking or suggesting advice, autonomous supervision of personnel falls far short of 50% time savings at equal quality.
Task automatability	claude-sonnet-5	1/5	Supervising and advising subordinate personnel involves interpersonal leadership, judgment, motivation, and contextual decision-making that current AI cannot perform end-to-end.There is no off-the-shelf system that manages people in this role.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Employment law, organizational hierarchy, labor regulations, and liability for personnel decisions create hard barriers; a human manager or supervisor is legally and organizationally required to oversee and advise employees.
Adoption barriers	claude-sonnet-5	4/5	Supervisory responsibility typically carries accountability, liability, and organizational structures requiring a human manager, creating strong organizational and legal barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The human supervisor's loaded wage typically far exceeds the cost of any current AI system for this task, and humans remain the only accountable party for personnel decisions and performance management.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for direct human supervision, so cost comparison favors the human entirely; AI cannot replace the function to compute a meaningful cost ratio in its favor.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs personnel supervision and mentoring autonomously. Current AI lacks the judgment, accountability, and human-relationship requirements inherent to the task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs supervisory or advisory people-management functions autonomously in a technical repair setting; this remains a human management function.

Solder loose connections, using soldering iron.

CI 5–10 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Medical device repair shops remain small-scale, non-digitized operations; adoption of advanced robotics for soldering in this sector is minimal and lagging, with most work still performed by human technicians.
Sector adoption velocity	claude-sonnet-5	1/5	Equipment repair is a physical, hands-on trade with low digitization and no meaningful robotic automation deployment trend for fine soldering tasks in this sector.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with defect detection via vision (identifying cold joints or misalignments), but the core manual soldering task itself offers limited augmentation value; the human technician remains essential for execution.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with diagnostics, guidance manuals, or identifying faulty connections, but offers little direct assistance to the physical act of soldering itself.
Task automatability	claude-haiku-4-5-20251001	1/5	Soldering requires precise spatial manipulation, heat control, and real-time sensory feedback in a physical environment. Current AI systems cannot reliably perform end-to-end robotic soldering on varied equipment without extensive task-specific setup and supervision.
Task automatability	claude-sonnet-5	1/5	This is a precise physical manipulation task requiring hand-eye coordination, tactile feedback, and fine motor control to solder connections on medical equipment; no off-the-shelf AI system can perform this physical action today.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Medical device repair is heavily regulated; FDA and quality standards typically require documented human expertise and accountability for solder joint integrity, creating legal and liability barriers to full automation.
Adoption barriers	claude-sonnet-5	3/5	While no formal licensing mandates a human solder specifically, medical equipment repair often falls under quality/safety regulations and liability concerns that require certified technician sign-off on such repairs.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic soldering systems capable of handling medical equipment repairs are capital-intensive and require specialized integration, making them far more expensive than a skilled technician's hourly labor for most repair scenarios.
Cost vs. human wage	claude-sonnet-5	1/5	Deploying robotic soldering for ad hoc, varied repair scenarios would require expensive custom robotics and setup far exceeding the cost of a technician using a hand soldering iron.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	While specialized industrial soldering robots exist, they are not general-purpose deployed products and require significant customization per task. Current general AI systems (vision + robotics) cannot independently perform reliable soldering on medical equipment in production settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed consumer or industrial product autonomously solders repair connections on medical devices in production; soldering robots exist only in narrow, fixed manufacturing-line contexts, not field repair.

Install medical equipment.

CI 0–5 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Medical device repair remains a hands-on, physically present service sector with minimal automation adoption. Technicians must be on-site, and regulatory requirements slow any shift toward automated or agent-based solutions.
Sector adoption velocity	claude-sonnet-5	1/5	Field service and equipment installation in healthcare facilities is a physical, low-digitization task with minimal AI/robotic adoption to date.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI can provide modest assistance through diagnostic tools, documentation systems, or remote guidance, but the physical installation task itself offers limited room for augmentation since the human must perform the core manual work.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with documentation, troubleshooting guides, or diagnostic checklists during installation, but offers limited help with the core physical installation work.
Task automatability	claude-haiku-4-5-20251001	1/5	Installation of medical equipment requires physical manipulation in varied, context-specific environments, real-time problem-solving, and precise calibration that current AI systems cannot perform end-to-end. While AI can assist with documentation or planning, the core task of physically placing, securing, and configuring equipment remains firmly in human hands.
Task automatability	claude-sonnet-5	1/5	Physical installation of medical equipment requires hands-on manipulation, wiring, calibration, and site-specific adjustments that current AI systems cannot perform without robotic embodiment, which is not deployed for this task.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Medical equipment installation is heavily regulated; most jurisdictions require licensed or certified medical equipment repair technicians to perform or sign off on installation. Liability, regulatory compliance, and safety requirements create strong legal and organizational barriers to full automation.
Adoption barriers	claude-sonnet-5	4/5	Medical equipment installation often requires certified technicians per manufacturer and regulatory requirements (e.g., FDA, hospital compliance), and liability for improper installation of life-critical equipment is high.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI systems capable of any meaningful portion of physical installation (advanced robotics, vision systems, integration overhead) would currently cost far more than hiring a skilled technician for the installation labor itself.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for physical installation, so the AI cost is effectively infinite relative to human labor for this task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably installs medical equipment independently today. The task requires dexterous robotics, site-specific adaptation, and equipment-specific knowledge that exceed current production systems' capabilities.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product installs medical equipment autonomously; this remains a physical, hands-on task performed by trained technicians.

Related occupations — Installation, Maintenance & Repair

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.