Healthcare AI forum panellists call for infrastructure, governance, and collaboration as prerequisites for equitable AI adoption

Senior health officials and researchers from across Asia and the Pacific used a panel Q&A session at the ADB-WHO Forum on Harnessing AI for Health Equity to press speakers on one of the field’s most persistent challenges: where to begin.

Representatives from Bhutan, Indonesia, Cambodia, South Korea, and Bangladesh — alongside a senior ADB health specialist based in Jakarta — posed questions ranging from investment sequencing and data security to medical education reform and the need for a regional collaborative framework.

The Forum on Harnessing AI for Health Equity is convening on 25–26 March at ADB Headquarters in Manila, bringing together senior health officials from 16 developing member countries alongside researchers, innovators, and policymakers from across Asia and the Pacific. The forum is funded through the High-Level Technology Fund with support from the Government of Japan.

Infrastructure before AI

A delegate from Bhutan’s Ministry of Health drew broad agreement when he asked whether countries still reliant on paper-based medical records should first build electronic health record (EHR) systems before investing in AI. Panellists broadly concurred that digital infrastructure is a prerequisite, though they cautioned against an all-or-nothing approach.

Associate Professor Chris Paton of the University of Auckland noted that AI scribing tools have seen rapid grassroots adoption in New Zealand with minimal infrastructure requirements, suggesting that some entry points exist even in lower-resource settings. A panellist representing China’s DXY platform urged developing countries to build on existing global models rather than develop solutions from scratch, recommending investment in people and digital literacy as the first priority.

Datuk Dr. Nor Fariza Ngah, Senior Consultant Ophthalmologist and Deputy Director-General of Health (Research & Technical Support) at Malaysia’s Ministry of Health, highlighted her team’s retinal imaging project as a model for low-cost, high-impact AI implementation — arguing that identifying a country’s disease burden and existing data assets before commissioning new infrastructure can yield significant results at comparatively modest cost.

Data security and fragmentation

Indonesia’s Ministry of Health raised concerns about data security as the country works to integrate AI with its electronic medical record systems, citing its Personal Data Protection regulations. Sankalp Khanna, Research Team Leader for Health Intelligence at the Australian e-Health Research Centre, CSIRO, acknowledged that cybersecurity risks are rising sharply, partly because AI tools themselves lower the technical barrier for malicious actors. He recommended open-source collaboration and caution as guard rails for new system deployments.

On fragmentation — a longstanding problem in health information systems across the region — Khanna described how two decades of work building AI-enabled hospital tools had produced systems that could not be shared across institutions without starting from scratch. He identified standards-based interoperability as the essential, if underappreciated, fix.

Permission before technology

One of the more striking contributions came from Natarajan Rajaraman, Managing Director of Equitech Collective in Singapore, who argued that the primary barrier to AI adoption in many healthcare settings is not technology or even infrastructure, but the absence of institutional permission to experiment. He suggested that creating an environment where clinicians feel safe to try and fail — drawing on freely available open-source tools — could do more to spark adoption than large capital investments.

Professor Hongsoo Kim of Seoul National University echoed a related theme on accessibility, noting that elderly and vulnerable populations had shown unexpectedly high acceptance of AI-enabled care tools, provided the technology was designed to require minimal learning on the user’s part.

The case for a regional data commons

Professor Kim, Director of Seoul National University’s AI in Health and Care Centre and a specialist in health policy and ageing, called for a collaborative regional framework to address AI development challenges disproportionately shaped by a small number of countries and frontier companies. A panellist from DXY proposed that WHO co-create a public dataset incorporating both international and locally contributed data from across member countries, offering DXY’s own data expertise as a contribution to such an initiative.

The forum moderator noted that the two-day event represented an opportunity to build lasting networks, and invited ADB and WHO to continue supporting member countries through both financial and technical channels.