# Endoscopy Technicians

> Maintain a sterile field to provide support for physicians and nurses during endoscopy procedures. Prepare and maintain instruments and equipment. May obtain specimens.

- **SOC code:** 31-9099.02
- **Canonical URL:** https://singulariki.com/roles/role-31-9099-02
- **Also known as:** Certified Endo Tech (Certified Endoscopy Technician), Certified Flexible Endoscope Reprocessor (CFER), Endoscopy Technician (Endoscopy Tech), GI Tech (Gastrointestinal Technician), Certified Endoscopic Reprocessor (CER), Certified Flexible Endoscopy Reprocessor (CFER), Endoscope Technician (Endoscope Tech), Endoscopy Specialty Technician (Endoscopy Specialty Tech)
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Clean, disinfect, or calibrate scopes or other endoscopic instruments according to manufacturer recommendations and facility standards.
- Collect specimens from patients, using standard medical procedures.
- Perform safety checks to verify proper equipment functioning.
- Maintain or repair endoscopic equipment.
- Assist physicians or registered nurses in the conduct of endoscopic procedures.
- Place devices, such as blood pressure cuffs, pulse oximeter sensors, nasal cannulas, surgical cautery pads, and cardiac monitoring electrodes, on patients to monitor vital signs.
- Prepare suites or rooms according to endoscopic procedure requirements.
- Maintain inventories of endoscopic equipment and supplies.
- Attend in-service training to validate or refresh basic professional skills.
- Conduct in-service training sessions to disseminate information regarding equipment or instruments.
- Position or transport patients in accordance with instructions from medical personnel.
- Read current literature, talk with colleagues, or participate in professional organizations or conferences to keep abreast of developments in endoscopy.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Near Vision _(ability)_
- Problem Sensitivity _(ability)_
- English Language _(knowledge)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_
- Written Comprehension _(ability)_
- Customer and Personal Service _(knowledge)_
- Medicine and Dentistry _(knowledge)_
- Critical Thinking _(essential_skill)_
- Education and Training _(knowledge)_

**Skills in demand:**
- English Language _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Active Listening _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Social Perceptiveness _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Finger Dexterity _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Writing _(Common Skill)_
- Active Learning _(Common Skill)_
- Time Management _(Common Skill)_

**Tools & technology:**
- MEDITECH software _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Office software _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Word _(hot technology)_
- Email software
- Patient electronic medical record EMR software
- Scheduling software

## AI exposure & outlook

- **AI task-overlap index:** 26th percentile (Low) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 52nd percentile (Moderate) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 21st percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 12th percentile (Low) — source: microsoft_applicability.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.5% growth (About average); 14.4k annual openings; 109.7k → 113.5k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $46,050; 103,650 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-31-9099-02_