Orthopedic Surgeons, Except Pediatric
29-1242.00Median wage $358,550/yr14,100 employed (US)Rank #842 of 923 scored · top 91% by substitution
Diagnose and perform surgery to treat and prevent rheumatic and other diseases in the musculoskeletal system.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution13
Exposure14
Augmentation56
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%13
panel mean rating 1.5/5 → substitution pressure 13/100
Technical feasibility todayw 20%16
panel mean rating 1.6/5 → substitution pressure 16/100
Cost vs. human wagew 15%10
panel mean rating 1.4/5 → substitution pressure 10/100
Adoption barriersw 20%inverted — strong barriers lower the score6
panel mean rating 4.8/5 (barrier strength) → substitution pressure 6/100
Sector adoption velocityw 10%23
panel mean rating 1.9/5 → substitution pressure 23/100
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.
Prepare case histories.
44CI 25–62 · exposure 45 · augmentation 75 · click for rater detail
Prepare case histories.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare remains a heavily regulated, cautious sector with strong human-in-the-loop norms around documentation. While some surgical practices pilot AI-assisted note generation, meaningful production adoption of fully autonomous case history creation remains limited due to liability and professional standards. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Healthcare has been a comparatively slow adopter of AI due to regulatory, privacy, and workflow integration challenges, though AI scribe tools are seeing growing pilot and early production use in outpatient and surgical settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools that draft case history components or auto-populate structured fields can reduce surgeon documentation burden modestly. However, because case history preparation is a relatively straightforward task compared to diagnosis and surgical decision-making, the productivity lift is incremental rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI scribes and summarization tools can substantially speed up drafting of case histories from dictation or prior records, letting surgeons focus on verification and patient care rather than manual writing. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Case history preparation involves synthesizing complex patient information, clinical judgment, and narrative coherence. While AI can extract structured data from records and generate drafts, the nuanced medical decision-making about what is clinically relevant, causally connected, and legally important requires orthopedic expertise that current systems cannot reliably perform end-to-end. |
| Task automatability | claude-sonnet-5 | 4/5 | Case history preparation involves synthesizing structured data (history, meds, prior visits, imaging notes) into a written narrative, which current LLMs can draft rapidly from EHR inputs or dictated notes with substantial time savings, though final review is needed. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and regulatory barriers are substantial: the surgeon must personally attest to the accuracy and completeness of the medical record, and case histories may be discoverable in litigation. Liability and documentation standards effectively require the orthopedic surgeon to review and certify, creating a hard barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | The surgeon remains legally responsible for the accuracy of the medical record, and case histories often require clinical judgment and verification, creating moderate liability and oversight barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI transcription and extraction tools exist but require substantial orthopedic surgeon review and correction. When accounting for oversight, validation, and the risk of errors, the all-in cost savings are modest compared to the surgeon's loaded wage, and errors in case history can create liability costs. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-assisted transcription/summarization tools cost a small fraction of a surgeon's or scribe's time per case, offering large cost savings even after accounting for review overhead. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products exist for medical transcription and structured data extraction, but case history preparation demands selective synthesis and clinical reasoning that current AI tools perform inconsistently. Regulatory constraints and liability concerns mean orthopedic surgeons rely on human review rather than deployed AI systems that autonomously generate case histories. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Ambient documentation and clinical note-generation products (e.g., AI scribes integrated with EHRs) are deployed in some surgical practices today, but accuracy and completeness for complex orthopedic case histories still require physician verification, limiting full reliability at scale. |
Manage surgery services, including planning, scheduling and coordination, determination of procedures, or procurement of supplies and equipment.
25CI 25–25 · exposure 25 · augmentation 63 · click for rater detail
Manage surgery services, including planning, scheduling and coordination, determination of procedures, or procurement of supplies and equipment.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare IT adoption is slower than information-sector peers; while scheduling software is common, autonomous management systems for surgery services remain in pilot phases rather than production deployment across most hospital systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare administration is adopting digital scheduling and supply-chain tools gradually, but clinical management functions in hospitals show slow, cautious AI adoption compared to tech-forward sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist surgical administrators by optimizing schedules, flagging supply shortages, and consolidating procedure data; these capabilities raise human productivity on coordination tasks, though human judgment on final scheduling and procedure decisions remains essential. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based scheduling optimization, inventory forecasting, and administrative assistants can meaningfully streamline the logistical aspects of managing surgical services while the surgeon retains clinical decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with scheduling optimization and supply inventory tracking, but surgical procedure determination and overall service management require medical judgment, liability accountability, and stakeholder coordination that remains fundamentally human-driven; no current system can autonomously manage the full scope at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | Scheduling and procurement subcomponents can be aided by software, but clinical procedure determination and overall service management require surgeon judgment and accountability that current AI cannot replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant regulatory and liability barriers exist: surgeons and hospital administrators face legal responsibility for surgical scheduling and safety; patient care requirements and institutional governance mandate human decision-making and sign-off on procedures and resource allocation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Surgical planning and procedure determination require licensed physician oversight due to liability, patient safety regulations, and credentialing requirements, though scheduling/logistics have fewer barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized healthcare scheduling and supply-chain systems are expensive to implement and integrate; the loaded cost of a surgical administrator or coordinator performing these tasks is often lower than the total cost of AI solutions plus required oversight and customization. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Administrative scheduling tools can be cheap, but the clinical decision-making and coordination responsibilities still require highly paid physician time, keeping overall cost comparable to human-driven processes. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Scheduling and procurement tools exist and see adoption, but end-to-end surgery service management remains largely manual; no mature product performs reliable autonomous planning and coordination of surgical procedures and equipment procurement at clinical-grade reliability. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | OR scheduling and supply-chain software exist and are used in hospitals, but integrated management of surgical services including procedure selection is not performed reliably by any deployed AI product. |
Order and interpret the results of laboratory tests and diagnostic imaging procedures.
25CI 20–30 · exposure 30 · augmentation 75 · click for rater detail
Order and interpret the results of laboratory tests and diagnostic imaging procedures.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption in orthopedic surgery remains limited to pilot and early-stage implementations of imaging AI. Most surgeons continue to order imaging and consult radiologists directly; organizational inertia in hospital systems and surgeon skepticism of AI-only interpretation slow deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare, especially surgical specialties, adopts AI diagnostic aids slowly due to regulatory approval processes, liability concerns, and integration into clinical workflows despite growing pilot use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augmentation is strong in this task: AI-powered radiology report drafting, automated fracture detection overlays, and real-time comparison tools meaningfully accelerate surgeon interpretation and reduce oversight time while surgeons retain clinical judgment and accountability. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted imaging analysis and lab result triage can meaningfully speed up preliminary review and flag anomalies, letting surgeons focus attention more efficiently while retaining final interpretive responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with image classification and some lab report analysis, orthopedic surgeons must integrate clinical context, patient history, and nuanced judgment to order tests appropriately and interpret results for surgical planning. Current AI lacks the contextual reasoning and accountability to autonomously manage the full ordering-and-interpretation workflow. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with image analysis and flag abnormalities, but full ordering plus clinical interpretation integrated with patient context still requires physician judgment and legal sign-off, so end-to-end automation at equal quality is not yet achieved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and liability barriers apply: ordering and interpreting diagnostic imaging is a licensed clinical function; diagnostic errors can lead to surgical complications and malpractice exposure. Regulatory bodies expect physician accountability for these decisions, making autonomous AI substitution legally and ethically constrained. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Ordering tests and rendering diagnostic interpretations that guide treatment are legally restricted to licensed physicians, with malpractice liability requiring physician accountability for the final read. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI imaging analysis and report generation incur licensing, integration, and oversight costs; however, orthopedic surgeons' time remains essential for clinical decision-making, limiting pure cost displacement. The combination of AI tool cost plus required surgeon oversight does not achieve order-of-magnitude savings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI imaging tools add incremental software licensing cost on top of, not instead of, physician review, so total cost per diagnostic episode is not meaningfully cheaper than the human-only pathway. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Mature AI products exist for specific tasks (e.g., radiology report generation, fracture detection on imaging) and are deployed in some hospitals, but they operate as assistive tools with radiologist review, not as autonomous decision-makers. Error rates and liability concerns prevent end-to-end autonomous deployment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | FDA-cleared imaging AI tools exist for specific findings (e.g., fracture detection) but are narrow decision-support aids, not autonomous interpreters used without radiologist/surgeon oversight in production workflows. |
Prescribe preoperative and postoperative treatments and procedures, such as sedatives, diets, antibiotics, or preparation and treatment of the patient's operative area.
18CI 16–20 · exposure 25 · augmentation 63 · click for rater detail
Prescribe preoperative and postoperative treatments and procedures, such as sedatives, diets, antibiotics, or preparation and treatment of the patient's operative area.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for independent prescription writing is minimal because legal and liability barriers prevent it; most orthopedic practice uses protocol templates and EHR decision support, but the surgeon remains the final decision-maker and signatory. No material displacement is occurring. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare overall adopts AI slowly due to regulatory, liability, and EHR integration friction; clinical decision support pilots exist but full production autonomy in prescribing is rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | EHR-integrated clinical decision support and protocol reminders do assist surgeons by suggesting evidence-based preoperative/postoperative regimens, reducing cognitive load and improving adherence to guidelines. However, the assistance is moderate in scope because the surgeon's experience and judgment remain central to personalized treatment selection. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools meaningfully assist by flagging drug interactions, suggesting standardized protocols, and speeding order-set creation, improving surgeon efficiency while the physician retains final authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in identifying standard pre/post-operative protocols and suggesting medications based on guidelines, the task requires individualized clinical judgment, patient-specific assessment (comorbidities, allergies, contraindications), and legal signing authority that only a licensed physician can provide. Current AI cannot reliably make these complex, patient-tailored prescriptions end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can suggest standard preop/postop protocols and drug regimens from clinical guidelines, but personalized judgment integrating comorbidities, allergies, and surgical specifics still requires physician decision-making and legal prescribing authority. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Prescribing is a legally regulated, licensed activity; only a physician can legally issue prescriptions, and malpractice liability for adverse outcomes rests on the prescriber. Regulatory frameworks (state medical boards, FDA) mandate human physician authority and responsibility, making substitution legally impossible. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Prescribing medications and directing patient treatment requires a licensed physician; this is tightly regulated by medical licensing boards and liability law, making autonomous AI prescribing legally prohibited. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI-assisted protocol suggestion and oversight remains modest compared to the surgeon's loaded wage, but the surgeon must review and sign every prescription regardless, so net time savings are negligible. AI does not materially reduce the cost per prescription issued. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI decision support is cheap to run, but it cannot replace the surgeon's prescribing role, so the human cost remains largely unchanged; savings are marginal (faster order entry) rather than substitutive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Clinical decision support systems exist to aid prescription decisions, but no deployed AI product independently prescribes preoperative/postoperative regimens in production; legal and malpractice liability require physician oversight and final authority. Existing tools function as aids, not autonomous performers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Clinical decision-support tools exist that surface guideline-based recommendations, but no deployed product independently prescribes treatments; physicians must review and authorize all orders in production EHR systems. |
Analyze patient's medical history, medication allergies, physical condition, and examination results to verify operation's necessity and to determine best procedure.
14CI 7–20 · exposure 17 · augmentation 63 · click for rater detail
Analyze patient's medical history, medication allergies, physical condition, and examination results to verify operation's necessity and to determine best procedure.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare adoption of AI for clinical decision-making is cautious and heavily regulated; while some surgical decision-support tools exist in pilots and limited production, widespread displacement of this core physician task remains nascent. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare, especially surgical specialties, is a slower-adopting sector for autonomous decision-making due to regulatory, liability, and safety concerns, though administrative AI tools are spreading faster than clinical decision tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by flagging medication allergies, summarizing imaging or test results, and presenting relevant surgical options, thereby improving the surgeon's efficiency in reviewing data—but the physician remains responsible for final judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by aggregating medical history, flagging allergy interactions, summarizing imaging/lab results, and suggesting differential considerations, improving efficiency while the surgeon retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | While AI can assist in reviewing medical history and examination results, the task requires synthesizing complex clinical judgment, patient-specific risk assessment, and determining surgical necessity—decisions that fundamentally depend on physician accountability and cannot be fully automated end-to-end today with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help synthesize records and flag relevant history/allergies, but final verification of surgical necessity and procedure choice requires clinical judgment, physical exam findings, and legal accountability that current systems cannot autonomously replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Licensing and liability requirements legally mandate that a licensed orthopedic surgeon must evaluate the patient and make the determination of surgical necessity; malpractice risk and regulatory standards create hard barriers to autonomous AI substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | This is a licensed medical decision with direct liability implications; only a credentialed surgeon can legally determine surgical necessity and select a procedure, creating a hard regulatory and liability barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The physician's liability, expertise, and irreplaceable judgment mean the human cost of oversight is high; AI tools reduce per-task time marginally but do not achieve cost parity with the full clinical evaluation a surgeon must perform. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted chart review and summarization can reduce time spent gathering information, but the surgeon must still perform the exam and judgment step, so overall cost savings versus the human specialist are modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Clinical decision support systems exist to help analyze patient data, but no deployed product reliably performs independent determination of surgical necessity or procedure selection without physician review and sign-off; current AI tools function as assistants rather than autonomous decision-makers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Clinical decision-support and summarization tools exist and are used in some EHR systems, but no deployed product independently verifies surgical necessity or selects operative procedures at scale in production. |
Refer patient to medical specialist or other practitioners when necessary.
14CI 7–20 · exposure 17 · augmentation 50 · click for rater detail
Refer patient to medical specialist or other practitioners when necessary.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While healthcare organizations are adopting some AI clinical support tools, actual physician workflow for referral decisions remains largely unchanged and non-automated in practice; adoption of AI that genuinely influences referral behavior is limited. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare, especially surgical specialties, adopts AI decision-support slowly due to regulatory, liability, and EHR integration hurdles compared to faster-moving information sector fields. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by surfacing relevant specialist information, highlighting guideline-recommended specialists for conditions, or flagging multi-disciplinary cases, moderately raising surgeon productivity in making referral decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help surface relevant specialist options, summarize patient history, and flag red-flag symptoms warranting referral, aiding but not replacing physician judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Referral decisions require clinical judgment about patient condition, specialist expertise, and individual patient factors that current AI cannot reliably perform end-to-end. While AI could assist in identifying specialist types, the decision to refer and which specialist remains a human clinical judgment task. |
| Task automatability | claude-sonnet-5 | 2/5 | Deciding to refer requires clinical judgment integrating exam findings, history, and specialist availability; AI can flag potential need but cannot reliably execute the full referral decision end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Referral decisions are a core function of the licensed physician's scope of practice and clinical authority; regulations require a licensed surgeon to make treatment and specialist referral decisions, creating a hard legal barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Referral decisions are a core physician responsibility with direct liability implications; only a licensed physician can legally make and document this clinical judgment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot replace this task end-to-end, so cost comparison is not applicable. Any AI assistance would add cost atop the surgeon's existing cognitive work rather than substitute it. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Any AI-assisted triage still requires physician review and sign-off, so cost savings are limited to administrative support rather than replacing the judgment-intensive decision itself. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs referral decisions independently; AI tools exist only to suggest specialist types or flag conditions, but orthopedic surgeons must make the final referral decision themselves based on complex clinical context. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Clinical decision support tools exist that suggest referrals or flag abnormal findings, but no deployed product autonomously makes referral decisions in orthopedic practice at scale. |
Examine instruments, equipment, and operating room to ensure sterility.
13CI 0–25 · exposure 13 · augmentation 38 · click for rater detail
Examine instruments, equipment, and operating room to ensure sterility.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare remains a laggard sector for AI deployment in surgical contexts due to regulatory scrutiny, high liability costs, and organizational conservatism; automated sterility checks are not yet in routine production use in operating rooms. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Surgical/OR environments show very low AI adoption for physical safety-critical inspection tasks; this remains entirely manual with no signs of automation trials in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision tools could assist staff by highlighting potential contamination risks or equipment defects for human review, moderately improving thoroughness and speed, though the human must retain final authority over sterile field certification. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some IoT/sensor systems can log sterilization cycle data or flag equipment status, offering minor assistance, but they do not meaningfully transform the human judgment-based inspection process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can identify some visible contamination or missing components, the task requires comprehensive sensory evaluation (visual, tactile inspection of sterile packaging, verification of autoclave indicators) and clinical judgment about sterility assurance that current systems cannot reliably perform end-to-end with 50% time saving. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection and tactile/visual verification of sterile fields, instruments, and OR conditions in a physical space—no current AI system can perform this end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and legal barriers exist: sterility certification and operating room preparation are often mandated by hospital protocols, nursing scope of practice, and surgical standards (e.g., AORN guidelines); human verification is typically required before surgery. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Sterility verification is a patient-safety-critical, legally and clinically mandated responsibility of licensed surgical staff, with strict liability and accreditation requirements preventing delegation to non-human systems. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI vision inspection systems would require infrastructure investment, maintenance, and overhead that likely exceeds the labor cost of existing surgical technician and nursing staff performing this gatekeeping role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical verification task, so AI cost is not comparable—human performance is required and cheaper than any hypothetical robotic sterility-checking system. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision can detect gross defects or damage, but no deployed product reliably performs the full sterility verification task independently; clinical environments still require trained human personnel to sign off on sterile field readiness due to patient safety criticality. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously verifies OR sterility; this remains a manual, protocol-driven human task performed by surgical staff. |
Diagnose bodily disorders and orthopedic conditions, and provide treatments, such as medicines and surgeries, in clinics, hospital wards, or operating rooms.
11CI 3–19 · exposure 13 · augmentation 63 · click for rater detail
Diagnose bodily disorders and orthopedic conditions, and provide treatments, such as medicines and surgeries, in clinics, hospital wards, or operating rooms.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Hospitals and surgical centers are adopting imaging AI and surgical robots in pilots and select implementations, but deep, production-scale replacement of physician decision-making remains rare. Most adoption is assistive rather than substitutive. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Surgical specialties adopt AI slowly for the core clinical/surgical acts themselves, though imaging and administrative support see growing pilot use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI imaging analysis, preoperative planning software, and surgical navigation systems meaningfully enhance orthopedic surgeons' diagnostic accuracy, surgical precision, and efficiency. These tools are actively deployed in modern practice to assist human decision-making and execution. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI assists with diagnostic imaging interpretation, preoperative planning, and documentation, meaningfully aiding but not replacing surgeon judgment and hands-on care. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with image analysis (X-rays, MRIs) and some diagnostic decision support, the task requires integrating complex patient history, physical examination findings, intraoperative judgment, and surgical execution—none of which current systems can perform end-to-end with the 50% time-saving threshold at equal quality. The surgical component alone remains firmly in human hands. |
| Task automatability | claude-sonnet-5 | 1/5 | This task combines physical examination, surgical execution, and high-stakes clinical judgment in physical space, none of which current AI can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Medical licensing, malpractice liability, surgical credentialing, and regulatory frameworks (FDA, state medical boards) mandate that a licensed physician must diagnose and perform or directly supervise surgical treatment. Legal responsibility cannot be delegated to AI. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Diagnosis and surgical treatment require licensed physician authorization, malpractice liability, and direct physical intervention, representing hard legal and safety barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of an orthopedic surgeon far exceeds AI inference and diagnostic support costs, but AI tools do not replace the surgeon's role; they complement it. Standalone AI costs for this integrated task would be a small fraction of human compensation, but cannot yet substitute for the full task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing surgery or full treatment, so cost comparison favors the human by default; any AI tools are additive costs, not replacements. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Diagnostic AI tools (imaging analysis) exist in production but with limitations in edge cases and require radiologist verification; no deployed system performs the full diagnosis-to-treatment-to-surgery cycle. Surgical robots assist but require surgeon control and decision-making throughout. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently diagnoses and treats orthopedic conditions or performs surgery; AI is at most a decision-support or imaging-analysis tool used by a surgeon. |
Diagnose or treat disorders of the musculoskeletal system.
9CI 3–16 · exposure 13 · augmentation 50 · click for rater detail
Diagnose or treat disorders of the musculoskeletal system.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Healthcare adoption of AI remains slower than tech/finance sectors; AI for orthopedic imaging is in pilot and early adoption phases in some institutions, but clinical adoption of autonomous diagnostic-treatment AI is minimal. Regulatory caution and liability concerns limit velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare, particularly surgical specialties, adopts AI slowly due to regulatory, safety, and liability constraints, though imaging-assist tools are gradually being piloted. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI demonstrably assists orthopedic surgeons on image interpretation (highlighting abnormalities, suggesting diagnoses), reducing review time and improving detection of subtle findings. However, augmentation is limited to diagnostic support; treatment planning and execution remain largely human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with diagnostic imaging analysis, treatment planning support, and administrative documentation, but the surgeon remains central to diagnosis and treatment decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with image analysis (X-rays, MRIs) and pattern recognition in diagnostic datasets, orthopedic diagnosis and treatment requires integration of imaging, physical examination, patient history, and real-time clinical judgment. Current AI systems cannot perform the full diagnostic-to-treatment workflow independently or achieve 50% time savings at equal quality end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | Diagnosing and treating musculoskeletal disorders requires physical examination, surgical intervention, and complex clinical judgment integrating imaging, patient history, and hands-on assessment that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and regulatory barriers exist: diagnosis and surgical treatment of musculoskeletal disorders require a licensed physician to perform and sign off on care. Malpractice liability, regulatory oversight (FDA for devices/software), and patient safety requirements create hard gatekeeping that prevents substitution without human licensure. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Medical licensure, malpractice liability, and legal requirements mandate that a licensed physician diagnose and treat patients, especially for surgical musculoskeletal care. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Orthopedic surgeon compensation (high salary) combined with the complexity of full diagnostic-treatment automation means AI inference and oversight costs do not yet approach the cost-effectiveness threshold. The task requires specialized expertise that commands premium labor rates. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of substituting for the surgeon's diagnostic and treatment role, so no meaningful cost comparison exists—human physicians remain the only viable providers. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI products exist for narrow subtasks (e.g., fracture detection in radiographs), but no deployed system reliably handles the full scope of diagnosing and treating musculoskeletal disorders in production. Real-world clinical integration requires handling edge cases, rare presentations, and multi-modal reasoning that current systems perform inconsistently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product independently diagnoses or treats musculoskeletal disorders; AI tools exist only as narrow decision-support aids for imaging interpretation, not full diagnostic/treatment substitutes. |
Examine patient to obtain information on medical condition and surgical risk.
9CI 3–16 · exposure 13 · augmentation 50 · click for rater detail
Examine patient to obtain information on medical condition and surgical risk.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While healthcare is digitizing, clinical examination in orthopedic surgery remains a human-intensive, hands-on practice. Adoption of AI for pre-operative risk assessment is slow and limited to supportive tools; replacement is negligible given the legal requirement for physician judgment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Surgical practice adopts AI mainly for imaging analysis, scheduling, and documentation, but the hands-on examination and risk assessment step itself sees minimal automation deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by rapidly summarizing patient history, flagging relevant imaging findings, and surfacing documented risk factors, helping surgeons make more informed assessments. However, augmentation is modest because the core task—physical examination and clinical judgment—remains primarily manual. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by pulling relevant medical history, flagging risk factors from records, or suggesting differential considerations, but the physical exam and final risk judgment remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with analyzing medical imaging and patient records, the physical examination component—palpation, range-of-motion assessment, patient interaction—cannot be fully automated today. AI might extract information from imaging but cannot replace the hands-on assessment and clinical judgment required to safely evaluate surgical risk. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical examination requires hands-on palpation, range-of-motion testing, and real-time clinical judgment about surgical risk that current AI cannot perform end-to-end without a human physically present and reasoning through findings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate that a licensed physician must perform or directly oversee the pre-operative medical examination and risk assessment; liability for missed surgical contraindications is borne by the physician. This creates hard barriers to full automation regardless of technical capability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Licensed physicians are legally required to examine patients and assess surgical risk before procedures, and liability for missed findings falls squarely on the human clinician, making this a hard-barrier task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of an orthopedic surgeon's time for this task is high; current AI tools require expert interpretation and human validation, adding cost rather than reducing it. The total integrated cost of AI-assisted assessment still exceeds human-only evaluation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot perform the physical examination component, there is no viable AI substitute cost to compare; the surgeon's exam remains fully human-performed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed AI products can support diagnostic imaging analysis and flag risk factors from records, but no system reliably performs the complete pre-operative assessment end-to-end in clinical practice. Current solutions are narrow (image reading only) and require substantial physician oversight and integration into existing workflows. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical patient examination; AI tools exist only for adjacent tasks like documentation or risk-score calculation from already-collected data, not the exam itself. |
Operate on patient's musculoskeletal system to correct deformities, repair injuries, prevent and treat diseases, or improve or restore patient's functions.
5CI 3–7 · exposure 5 · augmentation 63 · click for rater detail
Operate on patient's musculoskeletal system to correct deformities, repair injuries, prevent and treat diseases, or improve or restore patient's functions.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While surgical robotics (da Vinci, etc.) have been adopted in some centers for specific procedures, adoption remains limited, requires extensive surgeon training, and does not displace the surgeon—only augments. Broader, deeper adoption of autonomous surgical AI in production is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Surgical AI/robotics adoption is real but slow and incremental, gated by regulatory approval, hospital capital cycles, and training requirements, unlike fast-moving digital-only sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted imaging analysis, preoperative planning tools, and intraoperative navigation systems offer meaningful support to surgeons, though the core operative task itself remains human-driven and cannot be fully augmented by current systems. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Robotic-assisted surgery, preoperative planning AI, and image-guided navigation meaningfully enhance precision and outcomes while the surgeon remains fully in control. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Surgical operation on the musculoskeletal system requires real-time physical manipulation, decision-making under uncertainty, and adaptation to intraoperative findings that current AI cannot perform end-to-end. While AI can assist with imaging analysis or preoperative planning, the core operative task remains fundamentally human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | Surgical operation on the musculoskeletal system requires physical dexterity, real-time judgment, and manual manipulation that current AI cannot perform end-to-end; robotic systems are surgeon-controlled tools, not autonomous surgeons. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Performing surgery on patients is legally restricted to licensed physicians and surgeons; malpractice liability, patient safety regulations, and the requirement for human accountability create near-absolute legal and regulatory barriers to full automation of operative work. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Surgery is one of the most heavily regulated and licensed activities in medicine, requiring a credentialed surgeon to perform and bear legal/medical liability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current surgical AI and robotic systems cost millions of dollars per installation plus substantial maintenance and training, far exceeding the loaded cost of a qualified orthopedic surgeon for the actual operative work performed. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Surgical robotics and AI-assisted tools add significant capital, licensing, and maintenance costs on top of the surgeon's wage, making them more expensive per case, not cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system performs orthopedic surgery autonomously or with sufficient reliability to replace surgeon decision-making and execution in production settings. Surgical robots exist but require direct human control and cannot make independent operative judgments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Surgical robotic platforms (e.g., MAKO, ROSA) are deployed in production but function as precision-assist tools under direct surgeon control, not as autonomous task performers. |
Follow established surgical techniques during the operation.
3CI 3–3 · exposure 0 · augmentation 63 · click for rater detail
Follow established surgical techniques during the operation.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Surgical robotic adoption remains limited to high-end quaternary centers and is growing slowly. Most orthopedic procedures are still performed with traditional open or arthroscopic techniques by human surgeons, reflecting cautious, laggard sector adoption of surgical automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Surgical robotics adoption is growing but remains assistive and procedure-specific; healthcare overall lags in full AI-driven task automation due to safety and regulatory constraints. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted intraoperative navigation, imaging overlays, and robotic haptic feedback can help surgeons refine technique and reduce variability during defined steps of surgery. These represent meaningful but partial augmentation—the surgeon remains the primary operator and decision-maker. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Robotic-assisted surgical systems and pre-operative planning tools meaningfully enhance precision and consistency during procedures, though the surgeon remains fully in control and responsible for technique. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Surgical execution requires real-time sensorimotor control, anatomical adaptation, and immediate decision-making in a dynamic physical environment. Current AI cannot perform the manual/dexterous aspects of actual surgery end-to-end; robotic systems require continuous human operator control. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical execution of surgical procedures requires manual dexterity, real-time tactile feedback, and adaptive judgment that current AI systems cannot perform autonomously; no off-the-shelf system can execute the operation end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Orthopedic surgery is heavily regulated; surgeons must be licensed, credentialed, and legally responsible for patient outcomes. Malpractice liability, patient consent, and regulatory oversight (FDA, state boards) create hard barriers to autonomous substitution of the human surgeon's judgment and sign-off. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Surgery is strictly licensed, requires a credentialed surgeon to legally perform and bear liability, and involves direct physical intervention on a patient—among the strongest regulatory and liability barriers possible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Surgical robots and associated AI infrastructure cost millions of dollars in capital and ongoing maintenance, while the intraoperative labor cost of a single surgeon is modest relative to total case economics. Full autonomy would need to be far cheaper than human surgeons to close this gap, which is not the case today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing the surgery itself, so cost comparison favors the human surgeon entirely; robotic assistance adds cost rather than reducing it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system autonomously performs orthopedic surgery following established techniques. Surgical robots (da Vinci, etc.) are teleoperated tools requiring a surgeon's direct control; they do not operate independently and thus do not demonstrate task completion. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously performs orthopedic surgical techniques; surgical robots (e.g., Mako, ROSA) are surgeon-controlled tools, not autonomous operators. |
Conduct research to develop and test surgical techniques that can improve operating procedures and outcomes related to musculoskeletal injuries and diseases.
1CI 0–3 · exposure 0 · augmentation 63 · click for rater detail
Conduct research to develop and test surgical techniques that can improve operating procedures and outcomes related to musculoskeletal injuries and diseases.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Surgical research development is conducted by academic medical centers and large healthcare systems, sectors with slower AI adoption for high-stakes clinical innovation. The conservative nature of surgical validation and regulatory requirements make rapid AI-driven displacement implausible. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Medical research and surgery are slow-adopting sectors for full-task automation, though AI tools for literature review and data analysis are gradually being piloted in academic medical centers. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist with literature synthesis, statistical analysis of trial outcomes, and identification of patterns in outcome data, moderately raising research productivity. However, the core creative and validative work remains with the surgeon-researcher. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist with literature synthesis, biomechanical simulations, image analysis, and statistical evaluation of surgical outcomes, significantly speeding up parts of the research process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires original hypothesis generation, complex experimental design, clinical interpretation of outcomes, and professional judgment to evaluate surgical feasibility and safety—capabilities well beyond current AI systems. End-to-end automation would demand independent ability to conceive novel techniques and validate them clinically, which no deployed system can do. |
| Task automatability | claude-sonnet-5 | 1/5 | Surgical technique research requires hands-on experimentation, physical validation, cadaver/animal studies, and clinical trials that current AI cannot execute end-to-end; AI can only assist peripheral analytical steps. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Surgical research and technique development are heavily regulated by IRBs, surgical boards, and clinical trial frameworks that require licensed physicians to design, oversee, and validate the work. Liability for novel surgical approaches rests on credentialed professionals, creating hard legal barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Surgical innovation requires licensed physician expertise, IRB/ethical approval, regulatory oversight (FDA/clinical trial rules), and liability structures that make full automation legally and professionally infeasible. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task requires expert orthopedic surgeons (high-cost labor) combined with institutional research infrastructure, clinical trials, and ethical oversight. AI tools cannot substitute for the surgeon's expertise and professional judgment, making automation economically infeasible. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human researcher entirely; any AI use is supplementary, not a replacement reducing overall cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs independent surgical research or technique development. While AI assists with literature review and data analysis, the core task of developing, testing, and validating new surgical approaches remains entirely human-driven in research settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs surgical technique research and validation autonomously; this remains firmly in human expert domain with only literature/data analysis tools available. |
Direct and coordinate activities of nurses, assistants, specialists, residents, and other medical staff.
1CI 0–3 · exposure 0 · augmentation 25 · click for rater detail
Direct and coordinate activities of nurses, assistants, specialists, residents, and other medical staff.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Healthcare remains heavily regulated with strong human-authority requirements; surgical teams depend on real-time judgment and legal responsibility vested in the attending surgeon. No sector adoption of AI-directed surgical team coordination is evident. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare overall adopts AI slowly for clinical leadership functions, with adoption concentrated in documentation/diagnostics rather than team direction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Scheduling tools and dashboards can assist a surgeon in visualizing team availability or workflow, but meaningful augmentation is limited because the core task—making binding decisions about people and priorities—remains fundamentally a human leadership function. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with scheduling, task tracking, or communication logs, but offers only marginal support to the core leadership and coordination function. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing and coordinating medical staff requires real-time human judgment, interpersonal negotiation, conflict resolution, and accountability for team performance—none of which current AI can perform end-to-end. AI cannot make binding decisions about staff allocation, performance management, or clinical priority-setting. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing and coordinating a surgical/clinical team requires real-time leadership, physical presence, and situational authority that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory frameworks require a licensed physician to be clinically responsible for and oversee medical staff; a surgeon's authority to direct the team is tied to their licensure and professional liability. No AI can substitute for this legally mandated human accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Licensure, liability, and legal responsibility for directing patient care and supervising staff require a credentialed physician; this is a hard regulatory and legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of attempting AI-driven team coordination (including inevitable human oversight, error correction, and re-work) would far exceed the cost of a surgeon performing this task as part of their role, especially given the liability of failed coordination. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this coordination role, so no meaningful cost comparison favors AI; human leadership is required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs the full supervisory and coordination function of a surgeon directing a surgical team or clinical unit. Scheduling software exists but does not replace the human director's real-time decision-making and authority. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages or directs multidisciplinary medical staff activities in clinical settings; this remains firmly human-led. |
Provide consultation and surgical assistance to other physicians and surgeons.
1CI 0–3 · exposure 0 · augmentation 38 · click for rater detail
Provide consultation and surgical assistance to other physicians and surgeons.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task is performed by licensed specialists in highly regulated clinical settings where human autonomy and accountability are non-negotiable. Adoption of AI replacement is near-zero; the sector has not and will not adopt automation of this core function. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Surgical practice adopts AI slowly for imaging/diagnostics support, but consultation and intraoperative assistance remain almost entirely human-performed with minimal production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might provide limited support for preoperative planning or real-time reference retrieval during surgery, but the bulk of consultation and surgical assistance—judgment, communication, and physical presence—remains squarely human. Augmentation potential is modest. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with case review, imaging analysis, literature synthesis, and pre-op planning that informs consultations, though it doesn't touch the physical assistance component. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Consultation and surgical assistance require real-time clinical judgment, dialogue with other specialists, and hands-on presence in the operating room or clinic. Current AI cannot meaningfully replace the cognitive and physical components of this interpersonal, high-stakes task. |
| Task automatability | claude-sonnet-5 | 1/5 | Providing surgical consultation and hands-on assistance requires physical presence, dexterity, and real-time clinical judgment that current AI cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal requirements, medical licensing, malpractice liability, and regulatory frameworks (FDA, state medical boards) all mandate that only a licensed physician can provide consultation and surgical assistance. This task is protected by hard legal and professional barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Surgical consultation and assistance legally require licensed physicians with malpractice accountability, making this a hard-barrier task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a physician providing this service vastly exceeds what current AI inference and integration could offer; moreover, a human physician's liability and decision-making authority are irreplaceable by cost-effective AI. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so the comparison is moot; any oversight/decision-support tool adds cost on top of the surgeon rather than replacing them. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system performs surgical assistance or peer consultation in a reliable, production capacity. While AI can support diagnosis or treatment planning in limited contexts, it cannot substitute for a licensed physician providing guidance or assisting in surgery. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical surgical assistance or independently renders binding surgical consultations; AI is at most a research/decision-support aid. |
Related occupations — Healthcare Practitioners & Technical
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.