Orthodontists
29-1023.00Median wage $289,140/yr6,210 employed (US)Rank #713 of 923 scored · top 77% by substitution
Examine, diagnose, and treat dental malocclusions and oral cavity anomalies. Design and fabricate appliances to realign teeth and jaws to produce and maintain normal function and to improve appearance.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution19
Exposure19
Augmentation58
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
11 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%19
panel mean rating 1.8/5 → substitution pressure 19/100
Technical feasibility todayw 20%19
panel mean rating 1.8/5 → substitution pressure 19/100
Cost vs. human wagew 15%20
panel mean rating 1.8/5 → substitution pressure 20/100
Adoption barriersw 20%inverted — strong barriers lower the score13
panel mean rating 4.5/5 (barrier strength) → substitution pressure 13/100
Sector adoption velocityw 10%23
panel mean rating 1.9/5 → substitution pressure 23/100
Task breakdown (11 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 diagnostic and treatment records.
58CI 34–82 · exposure 66 · augmentation 88 · importance 4.8/5 · click for rater detail
Prepare diagnostic and treatment records.
58| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Healthcare and dental practices have rapidly adopted AI-assisted EHR documentation and data extraction tools; major systems (e.g., cloud-based orthodontic platforms, AI-powered scribe integration) are in production use across many practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/orthodontic practices are generally slower adopters of AI compared to information-sector fields, with AI imaging tools used only in early pilot or narrow diagnostic support roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI augments record preparation substantially by automating data entry, flagging missing fields, suggesting clinical measurements from digital images, and organizing findings into structured templates, allowing the orthodontist to focus on clinical judgment rather than clerical work. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted imaging analysis, 3D modeling, and treatment planning software meaningfully speed up record preparation and analysis while the orthodontist retains full clinical control and final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Preparing diagnostic and treatment records (e.g., compiling patient data, medical history, X-ray findings, photographs, measurements) can be substantially automated through AI-driven documentation systems that extract, organize, and format clinical information from various sources, achieving well over 50% time savings at equal quality with current systems. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can assist in generating drafts of diagnostic summaries, imaging analysis, and treatment plan documentation from structured data, but final record compilation requires clinical judgment and verification, limiting full end-to-end automation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | HIPAA compliance, medical malpractice liability, and regulatory requirements for authenticated records create meaningful oversight and integration friction; however, the task itself is not legally reserved to a licensed practitioner—clerical and AI assistance are standard practice. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Diagnostic and treatment records for orthodontic care are part of a licensed clinical process with legal, medical liability, and record-keeping requirements that mandate professional oversight and sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven documentation and record compilation costs (via EHR integration, OCR, and data processing) are orders of magnitude cheaper than the loaded hourly wage of an orthodontist or administrative staff member performing manual compilation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI imaging/analysis tools reduce some labor but still require licensed orthodontist review and integration with practice management systems, so all-in costs remain comparable to human-assisted workflows rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed clinical documentation and EHR systems with AI-assisted data extraction and record-generation exist in widespread use; however, medical-legal compliance requirements and the need for clinician review of diagnostic interpretations introduce some material variability in reliability and scope. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some dental AI imaging and charting tools exist (e.g., cephalometric analysis software), but comprehensive automated preparation of diagnostic and treatment records in production clinics is still narrow and supplementary rather than autonomous. |
Design and fabricate appliances, such as space maintainers, retainers, and labial and lingual arch wires.
26CI 25–28 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Design and fabricate appliances, such as space maintainers, retainers, and labial and lingual arch wires.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption of digital design and 3D printing in orthodontics is growing, with many practices piloting or integrating these tools, but end-to-end automation remains rare; most use is assistive rather than replacive. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Digital dentistry/orthodontics is a physically-oriented, clinically regulated field with slower and more uneven AI/digital tool adoption compared to purely information-based sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | Digital design software and 3D printing substantially enhance clinician productivity by accelerating visualization, iteration, and initial fabrication, allowing orthodontists to focus on clinical customization and patient-specific refinement. |
| Augmentation potential | claude-sonnet-5 | 4/5 | CAD software and digital scanning meaningfully speed up appliance design and allow simulation of tooth movement, giving orthodontists substantial productivity gains while they remain responsible for final design and fitting. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While CAD software can assist in designing orthodontic appliances and 3D printing can fabricate some components, the task requires iterative clinical judgment, material selection, and customization based on individual patient anatomy that AI cannot perform autonomously. Significant human oversight and hands-on adjustment remain essential. |
| Task automatability | claude-sonnet-5 | 2/5 | Digital design of appliances can be assisted by CAD/CAM software, but fabrication and fitting require physical dexterity, patient-specific adjustment, and clinical judgment that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Orthodontists are licensed professionals whose scope of practice is regulated; appliance design and fabrication require clinical judgment that is legally and professionally tied to the practitioner's license and liability. Patient safety and fit requirements create strong barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Appliance design and fitting typically require a licensed orthodontist's diagnosis and sign-off, given liability for improper fit affecting dental health, creating a substantial regulatory and professional barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Even with CAD and 3D printing, the total cost of equipment, materials, integration, and necessary clinical oversight remains comparable to or higher than the time cost of manual design and fabrication by an experienced orthodontist. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Digital design and 3D printing can reduce lab costs somewhat, but equipment, software licensing, and the need for skilled technician/orthodontist oversight keep costs from being dramatically lower than traditional fabrication. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD design tools and 3D printing systems exist in orthodontic practices, but they are assistive rather than autonomous; clinicians still make critical design decisions and manually refine fabricated appliances. No deployed system reliably performs the full design-and-fabricate workflow without expert human direction. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed CAD/CAM systems (e.g., digital retainer design, 3D-printed appliances) exist and are used in orthodontic practices, but they still require significant human design input, oversight, and manual finishing/fitting steps. |
Study diagnostic records, such as medical or dental histories, plaster models of the teeth, photos of a patient's face and teeth, and X-rays, to develop patient treatment plans.
23CI 20–25 · exposure 25 · augmentation 63 · importance 4.9/5 · click for rater detail
Study diagnostic records, such as medical or dental histories, plaster models of the teeth, photos of a patient's face and teeth, and X-rays, to develop patient treatment plans.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI in orthodontics remains experimental and limited to select practices and university settings; most practices still rely on manual analysis, and regulatory and liability concerns slow production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/orthodontic practices are typically small, physical-care settings with slower AI tool adoption compared to information-sector professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered analysis of X-rays and photos can assist orthodontists by highlighting anatomical landmarks, measuring angles, and flagging malocclusion patterns, improving efficiency in data interpretation while the orthodontist retains final treatment planning authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools for 3D model analysis, X-ray interpretation assistance, and treatment simulation meaningfully speed up and improve the diagnostic record review process while the orthodontist retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze individual diagnostic images (X-rays, photos) and extract anatomical measurements, developing a complete treatment plan requires integrating multiple data types, understanding patient-specific constraints, and making clinically nuanced decisions about sequencing and outcomes that current systems cannot reliably do end-to-end without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist in analyzing imaging and flagging measurements (e.g., cephalometric analysis, cast digitization) but synthesizing full diagnostic records into a defensible treatment plan requires clinical judgment beyond current off-the-shelf automation for full end-to-end replacement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Orthodontists are licensed professionals whose treatment plans carry liability; diagnosis and treatment planning are legally scoped to licensed practitioners, and clinical decision-making requires state-credentialed judgment that cannot be fully delegated to automated systems. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Treatment planning for a patient is a licensed clinical act; orthodontists must legally develop and sign off on it, making this a hard regulatory/liability barrier to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI diagnostic aids require significant infrastructure, validation, and integration costs, plus mandatory orthodontist review, making total system cost comparable to or exceeding the time saved versus direct clinician analysis. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized diagnostic AI software has licensing and integration costs, and still requires orthodontist review of every case, so all-in cost savings versus the clinician's time are modest, not order-of-magnitude better. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI products exist for segmenting teeth in X-rays and classifying malocclusion from photos, but no deployed system reliably generates complete clinical treatment plans from full diagnostic records at production quality; most tools function as analysis aids rather than autonomous planners. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some orthodontic AI software (e.g., cephalometric landmark detection, treatment simulation tools) exists in production but is narrow-scope, assistive, and not relied upon to independently generate full treatment plans. |
Provide patients with proposed treatment plans and cost estimates.
23CI 20–25 · exposure 25 · augmentation 50 · importance 4.8/5 · click for rater detail
Provide patients with proposed treatment plans and cost estimates.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Orthodontic practices are small-to-medium businesses with moderate digitization; adoption of AI-assisted planning tools is in early pilot phases rather than widespread production deployment. The sector lags behind high-tech information and finance sectors in AI integration. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/orthodontic practices are a small-business, in-person service sector with historically low digitization and slow AI adoption relative to information or finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by rapidly generating initial plan options, visualizing treatment stages, and automating cost calculations, moderately increasing the efficiency of an orthodontist's planning process. However, the human clinician remains essential for clinical judgment and patient-specific customization. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted imaging analysis, treatment simulation software (e.g., digital smile design tools), and cost-estimate templates can meaningfully speed up plan preparation and patient communication while the orthodontist retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate initial cost estimates and visualize treatment plans from imaging, the task requires clinical judgment about patient anatomy, risk factors, and treatment sequencing that current systems cannot reliably perform end-to-end. Human orthodontists must ultimately review and customize plans, making full automation infeasible at the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | Cost estimate generation could be templated, but formulating a defensible clinical treatment plan requires diagnostic judgment from imaging and exam findings that current AI cannot reliably perform end-to-end.a |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Orthodontists are licensed professionals in most jurisdictions, and treatment planning typically requires a licensed orthodontist's legal responsibility and signature. Patient liability, regulatory standards for clinical recommendations, and professional accountability create strong legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Treatment planning and cost communication in orthodontics require a licensed practitioner's diagnosis and informed consent process, which is legally and ethically mandated, creating a hard barrier to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI planning tools require integration with imaging systems, specialized training, and ongoing oversight by licensed orthodontists, keeping total system cost high relative to the time savings on plan generation alone. The cost advantage is minimal given the continued need for professional review and customization. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software-assisted plan drafting and cost templates are cheap, but the clinical judgment, patient consultation, and liability oversight still require an expensive licensed orthodontist, keeping overall cost comparable to human-only delivery. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools exist for initial treatment planning from 3D scans and generating cost estimates, but they remain narrow in scope and require substantial human review before patient presentation. No deployed systems independently produce clinically adequate treatment plans ready for direct patient use without orthodontist intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some dental practice software assists with treatment plan documentation and billing estimates, but no deployed product independently generates and presents full orthodontic treatment plans to patients in production at scale. |
Diagnose teeth and jaw or other dental-facial abnormalities.
18CI 11–25 · exposure 22 · augmentation 75 · importance 4.9/5 · click for rater detail
Diagnose teeth and jaw or other dental-facial abnormalities.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Orthodontia is a specialized, relationship-intensive field with relatively low digitization pressure compared to radiology or general medicine. Adoption of AI diagnostic tools remains limited to large practices and academic centers, with most orthodontists still relying on traditional workflows. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Healthcare and dental practices are historically slow adopters of diagnostic AI due to regulatory, liability, and workflow integration hurdles, with pilots more common than widespread production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted image analysis and anomaly detection can meaningfully augment an orthodontist's diagnostic workflow by highlighting potential abnormalities, speeding radiograph review, and improving consistency, while the clinician retains final diagnostic authority and clinical judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted imaging analysis, 3D modeling, and cephalometric tracing tools meaningfully speed up data gathering and pattern recognition, helping orthodontists reach diagnoses faster while retaining final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in analyzing dental radiographs and detecting some anomalies, the diagnosis of complex dental-facial abnormalities requires integration of patient history, clinical examination, 3D imaging interpretation, and judgment about treatment options. Current AI systems can flag potential issues from images but cannot replace the full diagnostic process, which involves tactile examination and nuanced clinical reasoning. |
| Task automatability | claude-sonnet-5 | 1/5 | Diagnosis requires physical examination, patient history integration, and clinical judgment about treatment planning that current AI cannot perform end-to-end without a licensed clinician's direct involvement. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Diagnosis of dental-facial abnormalities is a core licensed function in orthodontics; regulatory and liability frameworks require a licensed orthodontist to perform or legally sign off on diagnoses. Patients also typically expect human professional judgment for treatment planning. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Diagnosing dental-facial abnormalities is a licensed clinical act requiring a qualified dentist/orthodontist to examine, interpret, and legally sign off on findings. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deploying AI diagnostic assistance (software, integration, oversight by licensed orthodontists) remains costly relative to the diagnostic labor it displaces, especially when considering liability and the need for human validation of all findings. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI imaging analysis tools add cost on top of required orthodontist oversight, so total cost is not meaningfully lower than the human diagnostic process it supports. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI products exist (e.g., CAD/CAM systems, some imaging analysis tools) that can identify specific dental anomalies from scans with reasonable accuracy, but they operate in narrow domains and typically require orthodontist review and confirmation. No fully autonomous diagnostic system reliably replaces human orthodontist judgment in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for analyzing dental radiographs and identifying cephalometric landmarks, but no deployed product independently diagnoses dental-facial abnormalities without orthodontist review and confirmation. |
Instruct dental officers and technical assistants in orthodontic procedures and techniques.
18CI 11–25 · exposure 13 · augmentation 50 · importance 4.8/5 · click for rater detail
Instruct dental officers and technical assistants in orthodontic procedures and techniques.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Dental and orthodontic practices remain relatively conservative in technology adoption and rely on traditional apprenticeship models; pilot adoption of AI-based instruction is minimal and not yet established in production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/orthodontic practice adoption of AI is still nascent and mostly limited to imaging and administrative tools, not clinical instruction. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist an orthodontist by generating draft instructional materials, creating visual aids, or suggesting explanations, but the core interpersonal and demonstrative aspects of teaching remain human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can supplement teaching via simulations, videos, and reference materials, but the core instructive interaction remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could generate instructional content or video demonstrations, the task fundamentally requires interactive teaching, real-time feedback on technique, and adaptation to learner questions—elements current AI systems cannot reliably deliver end-to-end with 50% time savings at equivalent quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Teaching hands-on orthodontic procedures requires live demonstration, physical correction of technique, and clinical judgment that AI cannot replicate end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and professional standards require that clinical instruction and training sign-off be performed by licensed professionals; liability and licensure requirements create hard barriers to full automation of instruction duties. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Clinical training and certification of technique typically requires supervision by a licensed practitioner, and liability for improperly trained staff creates strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Creating instructional AI systems with integration and oversight would be costly relative to a licensed orthodontist's teaching time, especially when personalized feedback and live demonstration are necessary. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-generated training content is cheap to produce, but the actual instructional labor (supervision, correction, feedback) still requires the orthodontist's paid time, so overall savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can produce educational materials and simulate some procedural steps, but deployed products do not yet reliably conduct live, interactive instruction with correction of hands-on errors or nuanced feedback that a human instructor provides in clinical settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for an orthodontist directly instructing staff in clinical technique; at best AI provides supplementary training materials. |
Coordinate orthodontic services with other dental and medical services.
18CI 11–25 · exposure 13 · augmentation 50 · importance 4.6/5 · click for rater detail
Coordinate orthodontic services with other dental and medical services.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Orthodontic practices remain small, locally operated businesses with limited health-IT integration; adoption of AI for clinical coordination is minimal and most practices still rely on manual phone/fax and in-house staff coordination. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/medical practices are historically slow AI adopters for clinical coordination tasks, with digitization mostly limited to scheduling and billing software rather than judgment-based coordination. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by auto-generating referral letters, flagging scheduling conflicts, and summarizing patient histories for multi-provider discussion, but the orthodontist must retain final judgment on clinical coordination decisions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with drafting referral letters, summarizing patient records, and flagging scheduling conflicts, improving efficiency while the orthodontist retains decision-making control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could draft some coordination messages or organize referral documentation, the task requires significant human judgment about clinical priorities, patient-specific care plans, and dynamic negotiation with providers—capabilities that current systems lack at a level enabling 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Coordinating care requires clinical judgment, interprofessional communication, and relationship management across providers that current AI cannot autonomously execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | State licensing, liability for clinical coordination decisions, HIPAA compliance, and the need for a licensed professional to sign off on treatment-coordination choices create strong legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Coordinating medical/dental treatment plans involves licensed clinical judgment and liability for patient outcomes, requiring a credentialed professional to authorize and oversee decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI into existing clinical workflows would require substantial customization for each practice, and the human orthodontist or coordinator must oversee all coordination decisions, making the all-in cost approach parity or worse than manual handling. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While administrative sub-tasks like messaging or record transfer could be cheaply automated, the judgment-heavy coordination still requires a paid clinician, keeping overall costs comparable to human-only workflows. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably coordinates complex multi-provider orthodontic and medical care at scale; scheduling and referral automation exist but don't handle the clinical reasoning and relationship management this task demands. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages cross-provider orthodontic/medical care coordination; at best AI assists with scheduling or record-sharing logistics. |
Advise patients to comply with treatment plans.
13CI 0–25 · exposure 13 · augmentation 50 · importance 4.8/5 · click for rater detail
Advise patients to comply with treatment plans.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Orthodontistry is a licensed, relationship-intensive profession with high regulatory compliance and slow digital transformation. Current adoption is limited to administrative tools; clinical patient engagement remains dominated by direct human interaction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/orthodontic practices are moderate adopters of digital tools like automated appointment and compliance reminders, but adoption of AI for direct patient counseling remains nascent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by preparing patient education materials or flagging compliance-risk factors, but the core task of advising and persuading requires human clinical presence and relational trust; augmentation is minimal relative to the human-centered nature of compliance counseling. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven reminder systems, chatbots for FAQs, and treatment-tracking apps can meaningfully support and reinforce the orthodontist's compliance messaging between visits. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Advising patients to comply with treatment plans requires nuanced interpersonal judgment, motivational skill, and responsiveness to individual patient concerns—capabilities far beyond current AI systems. While AI could generate generic compliance messages, it cannot meaningfully replicate the personalized persuasion and trust-building essential to this clinical task. |
| Task automatability | claude-sonnet-5 | 2/5 | Advising patients requires real-time relational communication, tailored explanation, and trust-building that current AI cannot fully replicate end-to-end in a clinical encounter, though it could support scripting or reminders.rating limited to partial support only. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Compliance advice is a core clinical function that must be delivered by or under the direct supervision of a licensed orthodontist, with legal and liability responsibility. Regulatory and professional standards require human clinical judgment and accountability, creating hard barriers to autonomous AI substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Clinical advice from a licensed provider carries liability and professional standard-of-care obligations, and patients generally expect this guidance directly from their treating orthodontist. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | An orthodontist's compliance counseling is embedded in a high-value clinical relationship and legal accountability; automating it via AI would still require clinician oversight and liability coverage, making all-in cost higher than or comparable to direct human delivery. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While automated reminders/texts are cheap, the substantive advising component still requires clinician time, so overall cost savings are modest compared to full task replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs patient compliance counseling in orthodontic practice. AI chatbots exist but lack the clinical judgment, legal standing, and patient relationship context required; they remain research-stage for this clinical application. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Patient education chatbots and automated reminder systems exist, but no deployed product reliably substitutes for an orthodontist's personalized in-person compliance counseling. |
Examine patients to assess abnormalities of jaw development, tooth position, and other dental-facial structures.
9CI 3–16 · exposure 13 · augmentation 63 · importance 4.9/5 · click for rater detail
Examine patients to assess abnormalities of jaw development, tooth position, and other dental-facial structures.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While dental practices increasingly use AI-assisted imaging tools, these remain aids within a licensed provider's workflow rather than autonomous systems. Adoption in orthodontics is slow relative to information sectors, driven by regulatory constraints, liability concerns, and the hands-on nature of clinical practice. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Dental/orthodontic practices are moderate adopters of AI for imaging analysis but the core physical exam remains untouched by automation, and adoption of even assistive tools is still emerging in most practices. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered imaging analysis, anomaly flagging, and cephalometric measurement tools meaningfully assist orthodontists by accelerating image review, highlighting subtle findings, and standardizing measurements, thereby improving diagnostic throughput while the clinician retains judgment on treatment planning. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted cephalometric analysis, 3D scan interpretation, and imaging software can help orthodontists identify and quantify abnormalities faster, enhancing but not replacing the physical exam. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist in analyzing dental imaging (X-rays, scans) to flag potential abnormalities, but patient examination requires direct clinical assessment of jaw development, bite dynamics, and tactile/functional evaluation that AI cannot perform end-to-end. Current systems lack the multi-modal integration and physical assessment capability to meet the 50% time-saving threshold without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical clinical examination requiring hands-on palpation, direct visual inspection, and interpretation of a live patient cannot be performed end-to-end by current AI systems. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Patient examination for diagnostic assessment and treatment planning must be performed or directly overseen by a licensed orthodontist per state and federal dental practice acts; there is no pathway for AI substitution without violating scope-of-practice regulations and professional liability standards. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Diagnosing dental-facial abnormalities requires a licensed dentist/orthodontist, involves direct physical patient contact, and carries liability that mandates human sign-off under dental regulatory frameworks. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI imaging analysis tools require significant capital investment (integration with practice systems, training) and ongoing oversight by a licensed orthodontist; the loaded cost of deployed AI solutions plus necessary human review likely exceeds the cost of the orthodontist performing the examination directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot replace the physical examination component, so there is no viable cost comparison for full task substitution; any AI use is supplementary to, not a replacement for, the orthodontist's time. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed AI products exist for dental image analysis and anomaly detection (e.g., cephalometric analysis software), but they support rather than replace the core examination task. No production system performs independent diagnostic assessment at the scope and reliability required for treatment planning; clinician validation and hands-on examination remain essential. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs the physical examination itself; AI is at best used to analyze images or scans after a human has captured them, not to conduct the exam. |
Fit dental appliances in patients' mouths to alter the position and relationship of teeth and jaws or to realign teeth.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.9/5 · click for rater detail
Fit dental appliances in patients' mouths to alter the position and relationship of teeth and jaws or to realign teeth.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The orthodontic field remains highly traditional and manual; adoption of AI-driven automation for clinical appliance fitting is minimal, with the profession relying on trained human practitioners. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Dental/orthodontic practice is a low-digitization, hands-on physical service sector where AI adoption for direct clinical manipulation is essentially absent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with treatment planning and diagnostics (e.g., bite analysis from scans), but it offers limited real-time assistance during the manual fitting process itself, which remains predominantly human-driven. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted treatment planning and appliance design (e.g., digital scanning, aligner design software) meaningfully aids the broader workflow, though the physical fitting itself is unaided by AI. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Fitting dental appliances requires precise manual dexterity, real-time 3D spatial manipulation inside a patient's mouth, and adaptive problem-solving based on individual anatomy—capabilities that current AI systems, even with robotics, cannot reliably perform end-to-end in a clinical setting. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical fitting and adjustment of dental appliances requires fine motor manipulation inside a patient's mouth, which current AI systems cannot perform; this is a hands-on clinical procedure, not an information task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Orthodontists must be licensed dental professionals; appliance fitting is a legally regulated clinical procedure that requires human licensing, liability responsibility, and direct patient contact—strong legal and organizational barriers prevent substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | This is a licensed clinical procedure requiring a credentialed orthodontist, with direct patient contact, liability exposure, and regulatory oversight of dental practice. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of any robotic or AI-assisted fitting system, combined with ongoing maintenance, integration, and oversight, far exceeds the cost of a human orthodontist performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so no meaningful cost comparison exists; a human orthodontist is required regardless of cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product today can independently fit appliances in patients' mouths; this task remains firmly in the hands of licensed human orthodontists in clinical practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical appliance fitting; robotics for intraoral manipulation remains research-stage at best, not in clinical production. |
Adjust dental appliances to produce and maintain normal function.
0CI 0–0 · exposure 0 · augmentation 38 · importance 4.9/5 · click for rater detail
Adjust dental appliances to produce and maintain normal function.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This is a hands-on clinical task requiring direct patient contact and licensed professional judgment. Healthcare sectors, especially orthodontics, move cautiously on clinical automation, and no meaningful AI displacement of this task is occurring. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Clinical dental practice involving hands-on procedures shows minimal AI adoption for physical manipulation tasks, unlike administrative or diagnostic imaging aspects of dentistry. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist via 3D imaging analysis, treatment planning visualization, and progress monitoring to guide human decision-making about adjustments, but the actual hands-on adjustment task remains human-dependent and augmentation is partial. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with treatment planning, imaging analysis, or predicting appliance adjustments needed, but does not meaningfully augment the physical act of adjusting the appliance itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Adjusting dental appliances requires precise physical manipulation, intraoral assessment, and real-time clinical judgment about tooth movement and bite correction. Current AI cannot perform hands-on clinical procedures or navigate the complex mechanical and anatomical work required. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically adjusting braces, wires, or retainers in a patient's mouth requires fine motor manipulation and tactile judgment that current AI systems cannot perform; this is a hands-on clinical procedure, not a data or language task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Orthodontic appliance adjustment is a licensed clinical procedure that only qualified orthodontists or supervised dental professionals may legally perform. Liability, patient safety, and regulatory frameworks create hard legal and professional barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Adjusting dental appliances is a licensed clinical procedure requiring a credentialed orthodontist, with direct patient contact and liability for oral health outcomes. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task demands licensed professional labor (high cost) for fine motor control and clinical expertise. AI cannot substitute for this specialized manual work, and any AI assistance would add cost rather than reduce it. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so cost comparison favors the human orthodontist entirely. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently adjust physical dental appliances. While AI can support diagnosis via imaging analysis, the core adjustment task remains entirely dependent on licensed human orthodontists in clinical practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical orthodontic appliance adjustments; robotics for dental manipulation remains research-stage and not used clinically for this purpose. |
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.