AI Resilience Report forHealth Technologists and Technicians, All Other

In health tech jobs like respiratory therapy, AI tools are already helping with paperwork and machine control. For example, new “AI scribe” software can listen in on patient visits and automatically fill out charts and forms ^[1]. Sensors and algorithms on equipment can also predict problems – one study describes “predictive maintenance” that uses device data to warn techs before a machine breaks ^[2].

Likewise, some ventilators and oxygen machines have “closed-loop” modes that adjust airflow by themselves based on patient needs ^[1]. These tools save time, but people still set up and check them.

Hospitals move faster on new AI when it clearly saves time or money. For instance, human medical scribes (who took notes) are expensive and in high demand ^[1], so a working AI scribe that cuts paperwork is attractive. However, patient-care tasks face more delays.

Life-support equipment and health data are tightly regulated, so any AI must be proved safe ^[1]. Most studies on AI tools so far are small or experimental ^{[1] [3]}, so doctors and regulators go slowly. In practice, this means AI is adopted first for low-risk work (like filling forms or alerting about broken machines) and more cautiously for hands-on care.

Ethical rules and patient trust also slow things: people want a human nearby for life-and-death tasks. Overall, automating simple parts of the job can help, but hospitals will keep real techs on hand for expert decisions and teaching.