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.
- Perform administrative tasks, such as managing office functions and finances.72
- Conduct or direct research on hearing or balance topics and report findings to help in the development of procedures, technology, or treatments.70
- Evaluate hearing and balance disorders to determine diagnoses and courses of treatment.67
- Plan and conduct treatment programs for patients' hearing or balance problems, consulting with educators, physicians, nurses, psychologists, speech-language pathologists, and other health care personnel, as necessary.66
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.
- Fit, dispense, and repair assistive devices, such as hearing aids.01
- Evaluate hearing and balance disorders to determine diagnoses and courses of treatment.02
- Perform administrative tasks, such as managing office functions and finances.03
- Monitor patients' progress and provide ongoing observation of hearing or balance status.04
- Educate and supervise audiology students and health care personnel.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Hearing Aid Specialists
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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.
