Healthcare Technology Management leaders are piloting AI to transform device maintenance, asset management and clinical workflows, but success hinges on five practical lessons: prioritize high-quality, interoperable data; embed models into existing HTM workflows; establish cross-disciplinary governance; ensure explainability and regulatory compliance; and invest in cybersecurity and vendor management. Early deployments show AI-enabled predictive maintenance can reduce downtime, prioritize repairs by clinical impact and free biomedical engineers for higher-value tasks.
To move from pilots to scale, the article recommends phased rollouts, tight integration with EHR and facility systems, continuous model validation and clinician engagement to build trust. Measuring clinical and operational outcomes — equipment uptime, response times, costs and patient safety metrics — is essential. Vendors and HTM teams must align on data standards, service-level agreements and transparent performance metrics to realize measurable ROI.
