薬事・規制業務スペシャリスト
法律AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.121
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.36
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは60%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 100 件を表示| Task | Claude.aiRaw / share % |
|---|---|
Prepare responses to customer requests for information, such as product data, written regulatory affairs statements, surveys, or questionnaires.13-1041 | 0.041928.1 |
Examine records, reports, or other documents to establish facts or detect discrepancies.13-1041 | 0.01268.5 |
Provide information, technical assistance, or training to supervisors, managers, or employees on topics such as employee supervision, hiring, grievance procedures, or staff development.13-1041 | 0.01147.6 |
Participate in the recruitment of employees through job fairs, career days, or advertising plans.13-1041 | 0.01077.2 |
Interpret regulatory rules or rule changes and ensure that they are communicated through corporate policies and procedures.13-1041 | 0.00986.5 |
Prepare correspondence, reports of inspections or investigations or recommendations for action.13-1041 | 0.00896.0 |
Prepare correspondence to inform concerned parties of licensing decisions or appeals processes.13-1041 | 0.00885.9 |
Advise project teams on subjects such as premarket regulatory requirements, export and labeling requirements, or clinical study compliance issues.13-1041 | 0.00755.0 |
Prepare reports of activities, evaluations, recommendations, or decisions.13-1041 | 0.00684.5 |
Obtain and distribute updated information regarding domestic or international laws, guidelines, or standards.13-1041 | 0.00422.8 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Provide technical review of data or reports to be incorporated into regulatory submissions to assure scientific rigor, accuracy, and clarity of presentation.O*NET Task ID 18050 | 1.0 |
Prepare responses to customer requests for information, such as product data, written regulatory affairs statements, surveys, or questionnaires.O*NET Task ID 18064 | 1.0 |
Write or update standard operating procedures, work instructions, or policies.O*NET Task ID 18066 | 1.0 |
Communicate with regulatory agencies regarding pre-submission strategies, potential regulatory pathways, compliance test requirements, or clarification and follow-up of submissions under review.O*NET Task ID 18045 | 0.5 |
Coordinate, prepare, or review regulatory submissions for domestic or international projects.O*NET Task ID 18048 | 0.5 |
Interpret regulatory rules or rule changes and ensure that they are communicated through corporate policies and procedures.O*NET Task ID 18049 | 0.5 |
Review product promotional materials, labeling, batch records, specification sheets, or test methods for compliance with applicable regulations and policies.O*NET Task ID 18051 | 0.5 |
Advise project teams on subjects such as premarket regulatory requirements, export and labeling requirements, or clinical study compliance issues.O*NET Task ID 18052 | 0.5 |
Compile and maintain regulatory documentation databases or systems.O*NET Task ID 18053 | 0.5 |
Coordinate efforts associated with the preparation of regulatory documents or submissions.O*NET Task ID 18054 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page