A review published in npj Digital Medicine surveys artificial intelligence and automation in intravenous infusion systems. Its main conclusion is that most AI-enabled systems are still prototypes with limited clinical use. Where AI is used, it is mostly for error and anomaly detection rather than decision support, and many systems can flag a problem but still need a clinician to change the infusion rate.<\/p>\n
The authors also point to weak integration with electronic health records, few systems that follow the HL7 FHIR standard, and inconsistent data security around sensitive medication data. Computer vision for monitoring fluid levels and automated bottle switching are among the promising prototypes. The review proposes a framework linking patient monitoring, automated fluid delivery and predictive analytics, and calls for secure, clinically validated, feedback-controlled systems.<\/p>\n
Why it matters:<\/strong> the gap between smart-pump marketing and clinical reality is large. Safe adoption will depend on validation, connectivity standards and cybersecurity as much as on the algorithms.<\/p>\n