Why foot and ankle work stays in the room
Podiatry is a hands-on specialty. A podiatrist examines the foot, feels for tenderness and swelling, debrides a diabetic ulcer, performs surgery on bunions and hammertoes, and fits orthotic devices to a specific gait. Those steps need touch, judgment and a patient in the chair. That is the main reason the question of whether AI will replace podiatrists runs into a wall early.
The information side of the job is different. Reading an X-ray, checking a treatment history, drafting notes and coding a visit are all tasks software can shape. That is where erosion shows up first: not whole jobs vanishing, but fewer minutes spent on paperwork and first-pass image review.
Scale matters too. The Bureau of Labor Statistics counts about 9,680 podiatrists in the United States, with median pay near $160,300 (BLS, 2025) and projected employment change of about 2% from 2025 to 2035. It is a small, licensed, procedure-heavy field. Small fields attract less automation investment than fields with millions of workers doing repeatable screen work.
What AI does, what it helps with, and what it leaves to people
Some steps can run with light supervision: pulling prior records together, drafting visit notes, flagging an abnormality on a foot radiograph for the clinician to confirm, and handling scheduling and billing admin. The task split above shows the share of task time marked as something AI can do: 0%.
Assistive use is wider. Image tools can suggest a reading of a fracture or a bone change, and risk models can help spot a foot at risk of ulceration so the podiatrist decides what to do next. A clinician still signs the diagnosis, explains it and picks the treatment. The share of task time where AI assists rather than acts sits here: 10%.
The largest group is work that needs a person: the physical exam, surgery on the foot and ankle, wound care, injections, casting and the fitting and adjusting of corrective devices. That share is 90% of task time. On the coverage question, can AI do it, this job scores 13 out of 100, and how coverage is measured explains what that counts.
What has actually been tested
Our evidence grade for podiatry is D on an A to D scale. Grade D means there is no direct, published test of an AI system against qualified podiatrists doing this job’s real tasks. So we publish no parity number for it. Claims that software matches a foot and ankle specialist are not supported by a measured comparison yet.
What would settle it is specific: a study comparing AI-assisted and clinician-only diagnosis of foot pathology on the same cases, with outcomes tracked; a trial of autonomous wound assessment against in-person review; and reported results from devices cleared for use in routine podiatric care. Until that exists, the honest answer is uncertainty, not confidence in either direction. You can see how we grade evidence in our scoring method.
Good to know: a tool that reads one image well is not the same as a tool that can run a podiatric visit, and our grades keep those apart.
When this could realistically change
Most likely after 2042 (8 in 10 of our scenarios). The reasoning behind that window is set out in how we model replacement year.
Two things could pull it earlier. First, stronger and cleaner evidence: if image and risk tools are tested head to head with clinicians and hold up, more of the diagnostic step can move. Second, cost. Running a software tool is far cheaper per task than clinician time, so once a step is proven, it moves fast.
Two things hold it back. Most of the work has a physical component, and the robotics class it would need is a dexterous humanoid, which does not exist as a deployable product in a clinic. And podiatry is licensed and regulated: scope-of-practice rules, malpractice exposure and surgical privileges all keep a named clinician responsible for the decision and the procedure.
How to stay needed as a podiatrist
Lean into the tasks that carry the most human weight. Surgical and procedural work on the foot and ankle. Chronic wound and diabetic foot management, where repeat judgment over months decides the outcome. And device work, where fitting and adjusting an orthosis depends on watching a real person walk.
Two skills pay off. One is supervising machine output: reading a flagged image or risk score critically, and knowing when to override it. The other is explanation, because patients change behavior when the plan makes sense to them, and that is a conversation, not a report.
Nearby work is worth comparing. Closest by task mix are orthopedic surgeons, sports medicine physicians and orthotists and prosthetists, who share the device-fitting side of the job. You can also look at the wider diagnosing and treating practitioners family or the healthcare sector page to see how exposure differs across clinical roles.
Next step: put this job beside another on our side-by-side comparison, or scan the jobs that mostly need a person on the safest jobs list.