Medical Secretaries

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Medical Secretaries (ISCO-08 3344) score an average of 0.53 on a 0–1 exposure scale — more exposed than about 91% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Completing insurance and other claims forms;”

Scores 0.66 on the 2025 scale. The task of completing insurance and other claims forms involves data entry, standardized documentation, and processing information, similar to entering personal data for insurance contracts (semantically similar task with an automation score of 0.6). Given these structured and repetitive elements, Generative AI can significantly automate this task by managing data input, form generation, and even preliminary verification. The more routine elements are comparable to issuing certificates and duplicates or maintaining registers, which had scores around 0.625 to 0.68. However, human oversight remains necessary to address exceptions, ensure compliance with regulatory requirements, and handle complex queries or custom client interactions, much like developing and presenting insurance offers (automation score of 0.69). Considering the structured nature, capability of AI in document management, and inherent need for human intervention in nuanced areas, an adjusted automation score of 0.67 reflects a realistic balance of AI's benefits and the need for human input in a highly digitalized environment like Poland.

Moving fastest, 2023 → 2025

“Supervising the work of office support workers and other office staff.”

Model capability on this task changed by +0.14 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 3344, 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.

• Medical Secretaries and Administrative Assistants
• Medical Transcriptionists

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Medical Secretaries sit at the 91st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Medical Secretaries rank in the 91st 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 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.08 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Completing insurance and other claims forms;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Medical Secretaries". https://singulariki.com/gradient/3344-medical-secretaries.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.