AI impact · verified capabilities
How AI is changing the work of Nurse Midwives
Our reviewed registry links 7 verified AI capabilities to tasks that are part of this occupation. This shows where AI tools can already assist with specific tasks. It is not a forecast that the occupation will disappear, and it does not measure how widely any tool is used.
Last reviewed: 2026-09-27 · O*NET 29-1161.00 · Mapping confidence: medium
Task areas where verified capabilities connect
- Patient care
- Clinical documentation
- Diagnostics and imaging
- Logistics and routing
- Legal and document research
Verified capabilities (up to 5)
- EmergingEvidence: Strong
Clinical record summaries
What AI can assist with: Extracts patient information and summarizes records over time.
Practical meaning: this can speed up part of the task, but still needs human direction and review.
Human responsibility: you review every output and keep the final decision.
Source: AWS - EmergingEvidence: Strong
Real-time voice agents
What AI can assist with: Holds spoken conversations in real time for defined service or sales flows.
Practical meaning: this can execute a bounded workflow, but ownership, controls and edge cases remain with people.
Human responsibility: you set permissions and approval points and stay accountable for outcomes.
Source: Microsoft - EmergingEvidence: Strong
Medical coding assistance
What AI can assist with: Uses clinical language processing to suggest codes and abstract records.
Practical meaning: this can speed up part of the task, but still needs human direction and review.
Human responsibility: you review every output and keep the final decision.
Source: AWS - NowEvidence: Strong
Biomolecular interaction prediction
What AI can assist with: Predicts molecular structures and interactions beyond single proteins.
Practical meaning: this can speed up part of the task, but still needs human direction and review.
Human responsibility: you review every output and keep the final decision.
Source: Google DeepMind - NowEvidence: Strong
Machine-learning sepsis risk flagging
What AI can assist with: Clinical models continuously or periodically analyze patient data to flag elevated sepsis risk for healthcare-provider assessment.
Practical meaning: this can speed up part of the task, but still needs human direction and review.
Human responsibility: you review every output and keep the final decision.
Source: U.S. Food and Drug Administration
What remains human responsibility
Evidence shows a capability exists; it does not prove adoption in every workplace or full-task replacement.
Evidence limits
Each capability is verified from the cited source only. A source shows that a capability exists; it does not establish adoption, deployment depth or outcomes in any particular workplace.
Sources
- AWS — Generative AI and NLP for healthcare and life sciences
- Microsoft — Transparency note for real-time voice agents in Dynamics 365 Contact Center
- Microsoft — Computer-using agents, workflows, and real-time voice
- Google Cloud — Gemini Live API on Vertex AI
- Google DeepMind — AlphaFold
- U.S. Food and Drug Administration — 510(k) Premarket Notification: Bayesian Health Sepsis Flagging Device
- npj Digital Medicine — An artificial intelligence-powered learning health system to improve sepsis detection and quality of care: a before-and-after study
- Internal and Emergency Medicine — Real-time machine learning-assisted sepsis alert enhances the timeliness of antibiotic administration and diagnostic accuracy in emergency department patients with sepsis: a cluster-randomized trial
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