救急医
ヘルスケアAI露出度
- データ出典: BLS公表時点: 2026-08
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: ILO公表時点: 2025
0.27
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74%です。
- データ出典: OpenAI公表時点: 2023
0.353
0.000ここに掲載された値の範囲0.844尺度・母数・出典
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 201 件を表示| Task | Claude.aiRaw / share % |
|---|---|
Prepare comprehensive interpretive reports of findings.29-1069 | 0.075321.8 |
Develop individualized treatment plans for patients, considering patient preferences, clinical data, or the risks and benefits of therapies.29-1069 | 0.02637.6 |
Analyze and interpret results from tests such as microbial or parasite tests, urine analyses, hormonal assays, fine needle aspirations (FNAs), and polymerase chain reactions (PCRs).29-1069 | 0.02557.4 |
Interpret imaging data and confer with other medical specialists to formulate diagnoses.29-1069 | 0.02256.5 |
Communicate examination results or diagnostic information to referring physicians, patients, or families.29-1069 | 0.02116.1 |
Document the performance, interpretation, or outcomes of all procedures performed.29-1069 | 0.02076.0 |
Diagnose diseases or study medical conditions using techniques such as gross pathology, histology, cytology, cytopathology, clinical chemistry, immunology, flow cytometry, and molecular biology.29-1069 | 0.01855.3 |
Interpret diagnostic test results to make appropriate differential diagnoses.29-1069 | 0.01714.9 |
Examine microscopic samples to identify diseases or other abnormalities.29-1069 | 0.01394.0 |
Educate patients about diagnoses, prognoses, or treatments.29-1069 | 0.01203.5 |
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 |
|---|---|
Collect and record patient information, such as medical history or examination results, in electronic or handwritten medical records.O*NET Task ID 22717 | 1.0 |
Analyze records, examination information, or test results to diagnose medical conditions.O*NET Task ID 22715 | 0.5 |
Assess patients' pain levels or sedation requirements.O*NET Task ID 22716 | 0.5 |
Communicate likely outcomes of medical diseases or traumatic conditions to patients or their representatives.O*NET Task ID 22718 | 0.5 |
Conduct primary patient assessments that include information from prior medical care.O*NET Task ID 22719 | 0.5 |
Consult with hospitalists and other professionals, such as social workers, regarding patients' hospital admission, continued observation, transition of care, or discharge.O*NET Task ID 22720 | 0.5 |
Direct and coordinate activities of nurses, assistants, specialists, residents, and other medical staff.O*NET Task ID 22721 | 0.5 |
Discuss patients' treatment plans with physicians and other medical professionals.O*NET Task ID 22722 | 0.5 |
Evaluate patients' vital signs or laboratory data to determine emergency intervention needs and priority of treatment.O*NET Task ID 22723 | 0.5 |
Identify factors that may affect patient management, such as age, gender, barriers to communication, and underlying disease.O*NET Task ID 22724 | 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