Dentists

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 12 task statements that define Dentists (ISCO-08 2261) score an average of 0.15 on a 0–1 exposure scale — more exposed than about 17% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 12 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Supervising dental hygienists, dental assistants and other staff.”

Scores 0.27 on the 2025 scale. The task of supervising dental hygienists, dental assistants, and other staff involves managerial oversight, communication, and coordination, akin to supervisory roles provided in the context. Like tasks "Supervising other employees in the field of design and construction execution" (score: 0.275) and "Supervising auxiliary staff" (score: 0.275), this task requires considerable human interaction, decision-making, and interpersonal skills, which Generative AI cannot fully automate. While AI can assist with scheduling, monitoring compliance, and providing data analysis, it lacks the nuanced understanding and real-time problem-solving abilities needed for effective supervision. The task also shares some functionality with "Supervising other employees" (score: 0.35), indicating that it is slightly more automatable due to its structured nature and repetitive managerial tasks but still predominantly reliant on human skills. Given the context of Poland as a high-income country with prevalent technology use, Generative AI can facilitate but not replace human roles, situating the score in the moderate range. Thus, the score reflects AI’s auxiliary role in supervision tasks while acknowledging the essential human elements required.

Moving fastest, 2023 → 2025

“Providing surgical treatments such as extraction of teeth, biopsy of tissue, and performing orthodontic treatment;”

Model capability on this task changed by +0.10 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2261, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Dentists, General
• Dentists, All Other Specialists
• Oral and Maxillofacial Surgeons
• Orthodontists
• Prosthodontists

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Dentists sit at the 17th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Dentists rank in the 17th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Supervising dental hygienists, dental assistants and other staff.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Dentists". https://singulariki.com/gradient/2261-dentists.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.