South Korea approved a national strategy last month to embed AI across primary, emergency and hospital care, while a new Indian industry report flagged persistent gaps in data readiness, regulation and reimbursement that are holding back healthcare AI deployment at scale.
The South Korean plan, the AI Basic Healthcare Strategy, represents one of the most concrete government roadmaps for healthcare AI infrastructure. It includes a sovereign healthcare AI model, a national GPU-powered platform connecting 72 public medical institutions, and AI tools for clinical documentation, imaging and chronic disease management at the point of care.
South Korea's public healthcare AI highway
A central piece of the strategy is the Public Healthcare AI Highway, which will link 72 designated institutions to a national GPU-based Public Healthcare AI Platform. Nine institutions are expected to join a pilot network in the second half of 2026. That number will grow to 30 in 2027, with all 72 connected by 2029.
The government will also build a National Health and Medical Data Hub to securely combine healthcare data held across different institutions. This hub will underpin the planned Korean healthcare AI model, initially built for regional public hospitals. Work on the model begins next year.
At primary care facilities, South Korea plans to introduce AI packages covering medical imaging, clinical documentation and chronic disease management. It will run remote care pilots in medically underserved areas and build an integrated emergency AI platform - first tested in Daegu - that uses real-time information to support ambulance transfers.
National AI tools for patients and research
The government is strengthening medical record and imaging exchange through the My Health Record platform. It also plans a National AI Health Assistant that will use patient health information to explain prescriptions and screening results in everyday language and offer personalised health guidance.
Beyond care delivery, the strategy commits to national AI infrastructure for drug discovery and biomedical research. This includes the K-AI Drug Discovery Platform and a separate GPU-based drug development platform. A national biomedical big-data resource covering 120,000 people is expected by the second half of 2026, expanding to over 700,000 people by 2029 and more than 1 million by 2032.
On funding, the government said it will consider reimbursement models that reward healthcare outcomes delivered by AI technologies, alongside existing mechanisms that reimburse individual medical AI products. It also plans AI ethics and security guidelines, plus legislation governing healthcare data use, privacy and oversight. Regional hospitals will receive investment in research and data infrastructure, AI-specialised hospitals and cloud-based systems to replace ageing IT.
India's scaling problem
India has laid foundations for AI adoption through programmes including the Ayushman Bharat Digital Mission and IndiaAI Mission, according to an industry paper on AI in medtech released last month in New Delhi by Health and Family Welfare Minister J.P. Nadda. But the paper, jointly prepared by Praxis Global Alliance and the Federation of Indian Chambers of Commerce and Industry, said the country still lacks the ecosystem needed to move successful pilots into routine clinical deployment.
It identified three major gaps: AI-ready health data and evidence, lifecycle-based regulation for AI, and procurement and reimbursement mechanisms that recognise AI-enabled value. "Fragmented health data, evolving regulation and limited reimbursement pathways continue to constrain adoption despite advances in digital infrastructure, innovation and clinician acceptance," the paper said.
The report pointed to medtech as one of India's most immediate and scalable opportunities for AI, particularly through AI-enabled diagnostics that could extend specialist expertise into primary and secondary care. It also argued that India could draw on its software capabilities, digital public infrastructure, clinical diversity and growing medical device manufacturing sector to become a global hub for responsible AI for Healthcare development and manufacturing. Achieving that would require coordination among government, regulators, healthcare providers, payers, industry and academia, the paper emphasised.
Why this matters for healthcare professionals
South Korea's strategy is not a pilot programme - it is a funded, multi-year infrastructure build with specific timelines, institutional commitments and a reimbursement rethink. For healthcare leaders in other countries, it provides a reference architecture for what national AI readiness looks like. India's gaps, meanwhile, highlight the same obstacles that stall AI adoption in many health systems: data that cannot be combined, regulation that does not fit AI's lifecycle, and payment models that do not reward AI-driven outcomes. The contrast between the two countries makes one thing clear: digital infrastructure alone will not scale AI without parallel work on governance and reimbursement - including the kind of expertise covered in learning paths such as AI for Medical Billers.
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