Photo: Thomas M. Barwick INC/Getty Images
AI has moved firmly into the clinical mainstream across much of Asia-Pacific, and the question for 2026 is no longer whether it will be used in healthcare, but how its role will evolve as systems mature and governance frameworks take hold.
Where is AI headed in 2026? Healthcare IT News gathered thoughts from hospital and health IT leaders and experts in digital health and AI in healthcare in the region.
Where do you see AI headed in 2026?
Hu Zhongkai, Chief Technology Officer, Gushengtang
China
By yearend 2026, AI will complete its transformation from "tool" to "partner," driving two core healthcare changes: reconstructing resource allocation by enabling top-tier medical expertise – especially in TCM – to transcend geographical barriers, and accelerating traditional-modern medicine integration through patient data-driven preventive care and full-cycle treatment. Ultimately, AI will evolve into healthcare's "digital operating system," liberating physicians from non-clinical burdens to refocus on high-value decision-making and compassionate care, fundamentally restructuring healthcare service models beyond mere efficiency gains.
Serena Yong, CEO, Regency Specialist Hospital
Malaysia
By 2026, AI will be increasingly embedded into daily hospital operations rather than treated as a standalone technology. At Regency, we see AI evolving as a clinical support layer that enhances decision-making, improves efficiency, and enables more proactive and personalised care, while keeping clinicians firmly at the centre of patient care. Our approach to AI is practical and clinically driven. Building on initiatives such as our Next-Gen Smart Ward and the Hinotori Robotic-Assisted Surgery System, we are exploring AI applications in early warning systems, imaging support, patient flow optimisation, and administrative automation, with strong governance to ensure patient safety and data integrity.
Professor Juliana Chan Chung-ngor, Professor of Medicine and Therapeutics, Faculty of Medicine
Director, Hong Kong Institute of Diabetes and Obesity
The Chinese University of Hong Kong (CU Medicine)
Hong Kong
Although AI has increased the scope and reach of information, the latter must be assessed, critiqued, and scrutinised using human intelligence. Each patient or those at risk has a unique set of profiles with many unknowns or uncertainties that need to be gathered, documented, and processed during each clinical encounter to inform decision-making. Most healthcare-related AI tools have captured only some of this complex information and, as such, can only serve as an enabler for healthcare professionals to explain, engage, and help their clients make decisions based on their needs, values, and perspectives.
It is against this complexity that all relevant stakeholders in healthcare, whether public, patients, providers, payers, planners, or policymakers, should appreciate the potential utilities and limitations as well as benefits and harms of AI and work cohesively to use this powerful technology to benefit patients and those at risk.
Janine Cox, Executive Director, Data Information and Digital, Northern Queensland Primary Health Network
Australia
In 2026, we will see AI use by clinicians move beyond the use of scribes — provided we can embed strong governance, ethics and safety. Broader use cases will enable inclusive health ecosystems where every patient benefits from timely, data-driven decisions. The future is intelligent, ethical, and patient-centred.
Zongyuan Ge, PhD, Associate Professor, Faculty of Information Technology, Monash University
Australia
By 2026, the industry will transition from chatbots to agentic AI ecosystems. While current systems excel at solving isolated, instantaneous queries, they struggle with long-term planning and persistent memory. The next frontier is contextual understanding: AI agents that can maintain continuity across weeks of patient care or research cycles. These agentic frameworks will bridge the gap between simple automation and autonomous problem-solving, allowing AI to manage long-horizon dynamic problems, such as personalised chronic disease management, where understanding historical context is just as important as the immediate data.
Dr Eugene Loke, Medical Director, iAPPS Health Group
Singapore
AI moves from experimental to operational, shaped by the Ministry of Health's updated guidelines mandating validation, ongoing performance monitoring, and bias detection. The critical question shifts from capability to responsible implementation: how do we deploy AI within regulatory guardrails while maintaining patient safety? Singapore's governance-first approach will define how private practices scale these tools responsibly.
_
Editor's note: This article will be updated once more responses come in. Responses have also been edited for brevity.
