AI exposure: Cytotechnologists
Stain, mount, and study cells to detect evidence of cancer, hormonal abnormalities, and other pathological conditions following established standards and practices.
Reading this score
computedCytotechnologists is one of the unusual cases where capability and friction are both high. Current systems can produce a great deal of this work, with average capability across its tasks at 1.9 out of 4. Only 21.3% of the weighted task load comes out as exposed, because 29.3% of it runs into something structural. Jobs in this band tend not to disappear. They change shape, and the person stays.
What holds the line here is verification cost. Across this occupation's 13 tasks it averages 2.63 out of 3, the highest of the five friction dimensions. In plain terms, checking the output costs more than producing it. Where an undetected error is expensive, dangerous or irreversible, the economics change. Someone has to verify the work, and verifying can cost as much as doing it. This is the friction most likely to fall as tools for checking improve.
The most exposed thing this job does is Document specimens by verifying patients' and specimens' information, at 60.0%. The least is Assist pathologists or other physicians to collect cell samples by fine needle aspiration (FNA)..., at 0.0%. A gap of 60.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.
Within healthcare practitioner and technical occupations, this one is more exposed than most. The median across the 89 roles in the group is 18.4%, and only 27 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.
What would move this score. Of 13 tasks, 2 are currently banded exposed, 5 assisted and 6 untouched. For that distribution to shift materially would take a change in who is permitted to sign the work, which is a question for regulators rather than for engineers. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.
Where the score comes from
judgedEvery task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.
| Dimension | Mean | Scale |
|---|---|---|
| Capability | 1.95 | 0-4 |
| Embodiment | 1.81 | 0-3 |
| Presence | 1.33 | 0-3 |
| Accountability | 2.58 | 0-3 |
| Context | 1.28 | 0-3 |
| Verification cost | 2.63 | 0-3 |
What this means in practice
Roles in this band tend to change shape rather than disappear. The output gets drafted faster and the person moves toward review, judgment and accountability. The useful question is not whether to use these tools but who is trusted to sign off on what they produce.
Task by task
13 tasks, O*NET 31.0| Task | Exposed | Assisted | Untouched | Importance | Band |
|---|---|---|---|---|---|
| Document specimens by verifying patients' and specimens' information. | 60.0% | 40.0% | 0.0% | 5.00 | exposed |
| Submit slides with abnormal cell structures to pathologists for further examination. | 46.7% | 53.3% | 0.0% | 4.95 | assisted |
| Provide patient clinical data or microscopic findings to assist pathologists in the preparation of pathology reports. | 46.7% | 53.3% | 0.0% | 4.73 | assisted |
| Attend continuing education programs that address laboratory issues. | 30.0% | 20.0% | 50.0% | 3.70 | exposed |
| Examine cell samples to detect abnormalities in the color, shape, or size of cellular components and patterns. | 13.3% | 36.7% | 50.0% | 5.00 | assisted |
| Examine specimens, using microscopes, to evaluate specimen quality. | 13.3% | 36.7% | 50.0% | 4.82 | assisted |
| Examine specimens to detect abnormal hormone conditions. | 13.3% | 36.7% | 50.0% | 2.40 | assisted |
| Prepare and analyze samples, such as Papanicolaou (PAP) smear body fluids and fine needle aspirations (FNAs), to detect abnormal conditions. | 12.5% | 25.0% | 62.5% | 4.86 | untouched |
| Prepare cell samples by applying special staining techniques, such as chromosomal staining, to differentiate cells or cell components. | 10.0% | 15.0% | 75.0% | 4.27 | untouched |
| Maintain effective laboratory operations by adhering to standards of specimen collection, preparation, or laboratory safety. | 8.8% | 28.7% | 62.5% | 4.77 | untouched |
| Assign tasks or coordinate task assignments to ensure adequate performance of laboratory activities. | 5.0% | 20.0% | 75.0% | 4.00 | untouched |
| Adjust, maintain, or repair laboratory equipment, such as microscopes. | 2.4% | 5.9% | 91.7% | 4.14 | untouched |
| Assist pathologists or other physicians to collect cell samples by fine needle aspiration (FNA) biopsy or other method. | 0.0% | 0.0% | 100.0% | 4.68 | untouched |
Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.
Occupations either side of this one
The four closest scores in the same occupational family, then the four closest anywhere in the index.
Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.