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.
- Communicate with laboratories or investigators regarding laboratory findings.75
- Oversee subject enrollment to ensure that informed consent is properly obtained and documented.74
- Track enrollment status of subjects and document dropout information such as dropout causes and subject contact efforts.74
- Identify protocol problems, inform investigators of problems, or assist in problem resolution efforts, such as protocol revisions.74
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.
- Monitor study activities to ensure compliance with protocols and with all relevant local, federal, and state regulatory and institutional polices.01
- Assess eligibility of potential subjects through methods such as screening interviews, reviews of medical records, or discussions with physicians and nurses.02
- Schedule subjects for appointments, procedures, or inpatient stays as required by study protocols.03
- Maintain required records of study activity including case report forms, drug dispensation records, or regulatory forms.04
- Dispense medical devices or drugs, and calculate dosages and provide instructions as necessary.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Clinical Nurse Specialists
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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.
