Medical Appliance Technicians
AI is not doing this work today.
This occupation's activities barely register in measured AI usage. Generative AI is not doing this work today. The pressures that matter here are more likely economic or robotic than linguistic.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Construct, maintain, or repair medical supportive devices such as braces, orthotics and prosthetic devices, joints, arch supports, and other surgical and medical appliances.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Fabricate medical devices. | 23.7% | Not observed 0.00 |
| Take physical measurements of patients or clients. | 9.8% | Not observed 0.00 |
| Position workpieces or materials on equipment. | 8.2% | Not observed 0.00 |
| Join parts using soldering, welding, or brazing techniques. | 7.8% | Not observed 0.00 |
| Drill holes in earth or materials. | 7.8% | Not observed 0.00 |
| Maintain medical equipment or instruments. | 7.4% | Not observed 0.00 |
| Inspect completed work or finished products. | 6.4% | Not observed 0.00 |
| Read documents or materials to inform work processes. | 6.2% | Moderate 0.55 |
| Position materials or components for assembly. | 6.1% | Not observed 0.00 |
| Operate cutting or grinding equipment. | 5.9% | Not observed 0.00 |
| Smooth surfaces of objects or equipment. | 5.9% | Not observed 0.00 |
| Train others to use equipment or products. | 4.7% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is read documents or materials to inform work processes; the most important activity AI is not observed doing is fabricate medical devices. The plan below is built around exactly that split.
Low exposure is an asset. Spend it deliberately.
Next 30 days
- Don't buy the panic. This occupation's activities barely appear in AI usage data. Your near-term exposure is other people's headlines, not your tasks.
- Skim the gains anyway. Even here, an assistant helps with the thin admin layer: Read documents or materials to inform work processes is the one place the data shows any overlap.
Next 90 days
- Deepen the human core. Activities like Fabricate medical devices are measured as fully outside current AI. Mastery there is compounding, defensible value.
- Mentor the exposed. Colleagues in adjacent office-heavy roles are the ones facing redesign; understanding their tools makes you a better coordinator and leader.
The longer game
- Watch the actual frontier for this work: usually physical automation, demographics or regulation rather than language models. This dataset intentionally measures only generative AI.
- Revisit after major data updates. If this page's vintage changes and your band moves, the plan moves with it.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.