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Paramedic / EMT — tasks, one by one
The unit of analysis is the task, not the job title. Each one below carries its direction, whether the judgement rests on evidence or on platform inference, the reasoning, and what it does not establish.
Every task on this page#
Treating patients on scene
Still human-led✓ Evidence-backedResuscitation, airway care, giving drugs, splinting, and lifting and moving patients in homes and on roads.
The one machine found in this work is a helper, and the evidence for it is weak. A pragmatic trial across four UK ambulance services with 4,471 patients found no evidence that a mechanical chest-compression device improved 30-day survival compared with manual compressions. The US regulator cleared the device only as an adjunct to manual CPR when effective manual CPR is not possible, such as during transport or when there are too few crew.
One device and one trial; no record measures AI or robots in the rest of on-scene care.
Reading ECGs and assessing patients
Being augmented≈ Platform inferenceRecording and interpreting a 12-lead ECG, recognising a heart attack or stroke, and deciding where the patient needs to go.
AI is reading field ECGs in some services. In central Taiwan, EMTs at 14 fire stations sent ambulance ECGs to an AI that answered in 37.2 seconds on average, against 113.2 seconds for on-call physicians at 11 other stations, with high diagnostic accuracy. A machine-learning model for a hard-to-spot kind of heart attack outperformed practising clinicians and commercial systems, and combined with trained emergency personnel's judgement it helped correctly reclassify one in three patients with chest pain. The paramedic still records the ECG, treats the patient and decides.
One regional implementation evaluated by the hospital that built the AI, and one observational study; neither measures paramedics' workload.
Assessing callers by phone
Being augmented✓ Evidence-backedAssessing patients over the phone from the control room, giving advice, and deciding whether an ambulance is needed.
AI assists here but has not taken over. In a randomised trial in Copenhagen, a machine-learning model that listened to emergency calls recognised cardiac arrest more often than dispatchers did alone, yet alerting dispatchers did not significantly improve their recognition — 93.1% with alerts against 90.5% without. London's ambulance service says AI note-taking lets its control-room paramedics treat more people by phone.
One trial with dispatchers rather than paramedics, and an employer's own account; neither shows fewer staff.
Writing the patient record
Being augmented✓ Evidence-backedWriting up the assessment, treatment and handover in the patient-care record.
Drafting is moving to software; the record stays the paramedic's. London's ambulance service rolled out an AI tool that turns clinician–patient conversations in its control room into structured notes, which the paramedic checks and approves; crews in ambulances also tested it. A documentation software vendor says over 1,670 EMS agencies adopted its AI narrative feature. The UK regulator requires paramedics to record their decisions and reasoning appropriately.
An employer's account and a vendor's claim; neither measures accuracy of AI-drafted records or time saved per crew.
Driving and transporting patients
Still human-led≈ Platform inferenceDriving under emergency conditions, and moving and monitoring the patient on the way to hospital.
No record found shows self-driving ambulances or machines moving patients. The US statistics bureau projects EMT and paramedic employment to grow 6 percent from 2025 to 2035, faster than average, without mentioning AI or automation.
The absence of a record and a projection for one country; this is not a measurement of how ambulances are driven.