How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Diagnose diseases and deformities of the foot using medical histories, physical examinations, x-rays, and laboratory test results.71
- Treat bone, muscle, and joint disorders affecting the feet and ankles.68
- Prescribe medications, corrective devices, physical therapy, or surgery.68
- Surgically treat conditions such as corns, calluses, ingrown nails, tumors, shortened tendons, bunions, cysts, or abscesses.68
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Treat bone, muscle, and joint disorders affecting the feet and ankles.01
- Prescribe medications, corrective devices, physical therapy, or surgery.02
- Surgically treat conditions such as corns, calluses, ingrown nails, tumors, shortened tendons, bunions, cysts, or abscesses.03
- Perform administrative duties, such as hiring employees, ordering supplies, or keeping records.04
- Educate the public about the benefits of foot care through techniques such as speaking engagements, advertising, and other forums.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Physical Medicine and Rehabilitation Physicians
Closely related work
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Closely related work
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Related work
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Related work
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Related work
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Shares some work
Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
