AI adoption soars in APAC hospitals but stays deskbound, report says

Top use cases involved workflow optimisation and medical documentation.

Artificial intelligence (AI) use in Asia-Pacific (APAC) healthcare organisations is accelerating rapidly but remains concentrated in administrative functions rather than clinical care, according to a HIMSS report.

HIMSS found that 46% of respondents began implementing AI within the past 12 months, whilst only 23% have used it for more than three years.

Despite this, usage is already frequent, with 51% reporting daily or near-daily use and 25% using AI several times a week.

Generative AI tools dominate, used by 81% of respondents, far outpacing large language models (47%) and medical imaging applications (38%).

Use cases skew heavily operational—workflow optimisation (74%), medical documentation (65%) and administrative efficiency (64%) topped the list of areas where organisations seek value, and 80% cited improved operational efficiency as the most realised benefit.

Only 45% reported improved diagnostic accuracy, and just 33% cited enhanced patient engagement.

Workforce concern about displacement is low, with nearly half of respondents (49%) saying they are "not very concerned" or "not concerned at all" about AI reducing staffing need, as qualitative feedback consistently frame AI as a "co-pilot" rather than a replacement.

Financial cost is the leading barrier to broader adoption, cited by 67% of respondents, followed by data privacy (58%) and data security (52%).

On the investment side, Singapore (73%), South Korea (68%) and Thailand (67%) reported planned two-year AI spending above (US$150,000).

By contrast, nearly half of respondents in India and Indonesia (47% each) reported budgets below (US$25,000).

In terms of funding, 29% of organisations rely on digital transformation or innovation funds, 24% on government grants, and 19% on internal operational budgets.

GenAI and LLM tools, despite widespread use, show the largest gap between adoption and organisational support — 38% of respondents identified them as requiring additional backing, more than any other tool category, citing needs around prompt engineering and ethical governance frameworks.

Respondents identified a user-friendly interface (67%), seamless system integration (57%) and step-by-step usage guidelines (53%) as the most effective adoption enablers.

On training, hands-on workshops with live demonstrations were preferred by 60% of respondents, ahead of on-the-job coaching (51%) and case-based simulations (48%).

Moreover, governance leans on existing national frameworks rather than AI-specific regulation, as 34% of organisations cite national healthcare data protection laws as their primary governance reference, 25% cite Ministry of Health guidelines, and only 21% point to AI-specific national or regional regulatory frameworks.