Occupation score change
IMPACT 20physician exposure increased 2.0 points
AI Exposure Score moved from a previous calculation to 45/100 under methodology v1.0. 2 evidence item(s) contributed.
45/100
increased by +2.0 vs last
Moderate exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
2 sources
99 core tasks shown · 2 secondary hidden
Sports Medicine Physicians Moderate exposure: only 7 of 99 core tasks show high task automation potential : exposure 45/100 (moderate exposure).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
AI Exposure (composite)45/100
Methodology 2026-09-a · score v1.0 · input hash c0ef8905d9cae989
AI capability exposure63/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential43/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption40/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength47/100
Quantity and source quality of the evidence behind the score.
2 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Overview
Diagnose, treat, and help prevent injuries that occur during sporting events, athletic training, and physical activities.
Employment
≈800,000
approx. BLS OES
Median wage
≈$235,000
approx. BLS OES
Tasks tracked
101
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 5/5
core task · importance 4.9/5
core task · importance 4.9/5
core task · importance 4.9/5
core task · importance 4.9/5
core task · importance 4.9/5
core task · importance 4.9/5
core task · importance 4.9/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.8/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.6/5
core task · importance 4.5/5
core task · importance 4.5/5
core task · importance 4.5/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.4/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.3/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.1/5
core task · importance 4.1/5
core task · importance 4.1/5
core task · importance 4.1/5
- Inform coaches, trainers, or other interested parties regarding the medical conditions of athletes.22
core task · importance 4.1/5
core task · importance 4/5
core task · importance 4/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.8/5
core task · importance 3.8/5
core task · importance 3.6/5
core task · importance 3.6/5
core task · importance 3.6/5
core task · importance 3.5/5
- Advise athletes on ways that substances, such as herbal remedies, could affect drug testing results.30
core task · importance 3.4/5
core task · importance 3.3/5
core task · importance 3.1/5
Evidence
Evidence trail
2 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
- adoptionneutralconfidence 80/100 · magnitude 402 Jul 2024
The OECD Employment Outlook reports that occupations most exposed to AI are predominantly high-skill, and that exposure does not equal job loss, while noting emerging evidence of effects on task composition and entry-level work.
Cross-country official analysis. Used to inform the interpretation that exposure is not a prediction of unemployment.
Source: OECD — Employment Outlook 2024 · Updated 2 Jul 2024
- researchincreasesconfidence 78/100 · magnitude 521 Apr 2021
The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.
Important because it establishes the *direction* of exposure: much AI exposure sits in high-skill work rather than routine manual work. Included here as a methodology reference and an exposure input.
Source: Felten, Raj & Seamans — Occupational, industry, and geographic exposure to artificial intelligence (Strategic Management Journal) · Updated 1 Apr 2021
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 5 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
- Research publishedIMPACT 40
Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts
arXiv:2609.38600v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in clinical settings, it is critical to evaluate their reliability under realistic variation in clinical text. We study this question in clinical triage, comparing LLMs to practicing physicians under text perturbations that preserve the underlying clinical setting. We introduce a benchmark of over 6,000 clinical scenarios, 7,000 physician annotations, and 225,000 model responses. Using this benchmark, we make two key observations. First, LLMs are more likely than physicians to recommend unnece
- AI adoption signal
AI exposure is highest in high-skill occupations, with polarised effects
The OECD Employment Outlook reports that occupations most exposed to AI are predominantly high-skill, and that exposure does not equal job loss, while noting emerging evidence of effects on task composition and entry-level work.
- Research publishedIMPACT 52
AI Occupational Exposure Index built from 10 linked electronic advances
The authors construct an AI Occupational Exposure Index by linking 10 AI application categories to 52 O*NET abilities, finding that high-exposure occupations are more common in high-wage, high-education industries.
Related
Related Occupations
Occupations sharing major SOC group (29) with Sports Medicine Physicians.
faq
Frequently Asked Questions
Common questions regarding Sports Medicine Physicians and AI exposure.
Will AI replace Sports Medicine Physicians?
Current AI models show an exposure score of 45/100 for Sports Medicine Physicians. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Moderate exposure reflects assistance in routine information-handling while core responsibilities require human judgment.
Which tasks in Sports Medicine Physicians have the highest AI exposure?
The most exposed tasks for Sports Medicine Physicians include "Write patient discharge summaries and send them to primary care physicians." (85% exposure) and "Record athletes' medical care information, and maintain medical records." (85% exposure).
What aspects of Sports Medicine Physicians can AI not do yet?
Tasks with the lowest AI exposure for Sports Medicine Physicians include "Conduct physical tests, such as functional capacity evaluations, to determine injured workers' capabilities to perform…" (5% exposure) and "Perform allergen provocation tests such as nasal, conjunctival, bronchial, oral, food, or medication challenges." (5% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.