Health organisations across the Asia‑Pacific are struggling to move artificial intelligence projects from pilot to production despite clear potential to improve efficiency, diagnosis and care coordination. Many early deployments show promise in triage, imaging analysis and administrative automation, but they remain confined to trial environments because outcomes vary across sites, integration with clinical workflows and electronic health records is weak, and real‑world validation is limited.
Interviewees cite barriers including fragmented data, interoperability gaps, limited IT infrastructure, regulatory uncertainty, vendor fragmentation and clinicians’ scepticism fueled by opaque algorithms and unclear ROI. Experts recommend pragmatic, use‑case driven pilots designed for scale, stronger data governance, clinician co‑design, standardised evaluation metrics and public‑private partnerships to fund integration work. Without these changes, many pilots will fail to deliver system‑level efficiency gains or equitable clinical benefit.
