As of 2026, robot-assisted surgery AI has established itself as the application recording the highest market share in the global healthcare AI market.
I. Healthcare AI Beyond the Tipping Point
In 2026, healthcare artificial intelligence (AI) is no longer in the realm of pilot projects. Sixty-three percent (63%) of global healthcare and life sciences experts are already utilizing AI in their practical work, and an additional 31% are in the pilot or evaluation stage. This means that the healthcare sector is significantly ahead of other industries, which have an average AI adoption rate of about 50%.
According to NVIDIA's '2026 State of AI in Healthcare and Life Sciences' report, more than half of medical institutions that quantitatively evaluated AI recorded more than double the return on investment (ROI), with the average ROI reaching $3.20 for every $1 invested within 14 months.
The expansion rate of the market size is also at an unprecedented level. According to major global reports, the healthcare AI market is projected to expand from about $37 billion to $39 billion in 2025 to around $50 billion in 2026, maintaining a compound annual growth rate (CAGR) of approximately 38% to 39%, with multiple market research agencies estimating it will exceed $500 billion by 2033.
The core driver of this growth is not mere technological expectations. With the simultaneous pressures of healthcare workforce shortages, structural expansion of administrative costs, and demands for Value-Based Care, AI adoption is becoming a competitive necessity rather than a choice.
Domestically, against the backdrop of the Lee Jae-myung administration's AI national strategy, policy acceleration is in full swing, with the Ministry of Health and Welfare establishing a healthcare AI job training program and expanding healthcare AI data utilization vouchers fivefold from 8 tasks in 2025 to 40 tasks in 2026.
While leading domestic companies such as Lunit and VUNO achieved record-high earnings in 2025 and are growing their presence in the global market, healthcare data standardization and national health insurance reimbursement issues remain structural challenges.