AI Delegation Index

What work could you hand off to an AI agent?

Orthodontists

Moderate

AI agents could support several recurring tasks in this job, while people continue to lead decisions and handle exceptions.

Where agents can help most

  1. 1

    Update patient chart after visit

    Use an agent to turn your visit notes, exam findings, appliance adjustments, and follow-up timing into a draft post-visit chart entry.

  2. 2

    Follow up on appliance use

    Use an agent to review missed appointments, wear reports, hygiene notes, and progress photos or scans, then draft a follow-up message for patients who need a reminder about appliance use.

  3. 3

    Revise treatment plan from progress

    Use an agent to compare progress photos, scans, appliance wear notes, and prior records, then draft a revised visit plan and patient instructions for your review.

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O*NET-SOC 29-1023.00 · #163 of 923

Result context

How to read this result

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.

National position
#163 of 923 occupations
Top 18% of occupations
Overall AI delegation potential
Moderate
Potential of score-contributing workflows
Strong · 78/100
Meaningful work covered
78%

The overall rating combines how useful the best agent workflows are with how much of the occupation they address. It is not an estimate of job automation or replacement.

Recommended agent uses

3 workflows you could delegate to AI

1

Update patient chart after visit

How you could use an agent

Use an agent to turn your visit notes, exam findings, appliance adjustments, and follow-up timing into a draft post-visit chart entry. It can compare the draft with the source notes, spot missing items, and leave you with a clean record to review and finish.

Where you stay involved

You confirm that the chart matches what actually happened, fix anything the draft missed, and approve the final record before it goes in the patient file.

Review level: Medium

2

Follow up on appliance use

How you could use an agent

Use an agent to review missed appointments, wear reports, hygiene notes, and progress photos or scans, then draft a follow-up message for patients who need a reminder about appliance use. It can flag cases where the notes suggest a problem and prepare a list of patients who need a callback or another visit.

Where you stay involved

You decide whether the situation looks routine or needs your attention, adjust the message, and handle any treatment changes or clinical concerns yourself.

Review level: Medium

3

Revise treatment plan from progress

How you could use an agent

Use an agent to compare progress photos, scans, appliance wear notes, and prior records, then draft a revised visit plan and patient instructions for your review. It can summarize what seems on track, what needs closer attention, and what should be discussed at the next appointment.

Where you stay involved

You review the draft against your own exam findings, decide whether the plan should change, and make any treatment changes or physical adjustments yourself.

Review level: High

Supporting analysis

Why this occupation's AI delegation potential is Moderate

Underlying methodology score

73 / 100

This technical score determines the qualitative rating; it is not an estimate of the share of the occupation that can be automated.

Importance & frequency94
AI capability74
Digital actionability82
End-to-end leverage61
Safety & reversibility73
Meaningful-work coverage
78%
Physical-work modifier
Limited
Safety modifier
Limited
Qualitative judgment
No material constraint
O*NET task evidence
11 tasks

O*NET 31.0 · methodology 3.3.0. Every workflow passes an action-level physical-execution and protected human-and-veterinary clinical-action gate. Documentation workflows must own a complete digital loop and use digital task evidence only; support-only workflows are disclosed separately and excluded from scoring. Artistic, editorial, normative, and policy-dependent work receives a transparent human-judgment constraint. National ranking within 923 scored O*NET occupations under methodology 3.3.0.

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