Booking a flight, paying bills, or ordering groceries online takes minutes. Yet, scheduling a doctor’s appointment, accessing lab results, or sharing medical records often feels like stepping back in time. Health care remains one of the least digitized sectors. It is fragmented, paper-heavy, and frustratingly slow to change.

Generative artificial intelligence may finally be the catalyst that propels health systems into the digital age. By creating new text, images, and even synthetic data, generative AI can transform how health workers document, diagnose, communicate, and innovate.

Unlike traditional AI, which simply analyzes data, generative AI can synthesize information and generate solutions. In health care, that means it can summarize complex medical histories, draft discharge notes, or generate realistic training cases for clinicians.

It can help radiologists detect early disease patterns, accelerate drug discovery by designing new molecules, and even simulate public health scenarios for better planning.

The potential is enormous, especially in Asia and the Pacific, where health systems face chronic shortages of health workers, fragmented data, and rising demand. By automating routine tasks, generative AI can ease the administrative burden, allowing doctors and nurses to focus on what matters most: patients.

We are seeing both momentum and caution. Across the world, governments are beginning to set rules. The European Union’s AI Act now classifies health applications as "high-risk," requiring safety, transparency, and human oversight.

The United States Food and Drug Administration has issued guidance for AI-based medical devices and software. The People’s Republic of China’s interim measures regulate generative AI services and their use in the public domain. Italy’s new AI law focuses on privacy and accountability, while the World Health Organization has issued a global call for "responsible and ethical AI in health."

Türkiye’s draft AI law proposes similar risk-based regulation, while its National AI Strategy promotes generative AI in preventive medicine and vaccination. Pakistan is piloting AI in tuberculosis and cancer screening and has included health AI in its National AI Policy 2025, which calls for centers of excellence and clinician training.

Generative AI can ease the administrative burden, allowing doctors and nurses to focus on what matters most: patients.

These developments show that countries recognize both the opportunity and the risk. Innovation must not outpace ethics and safety.

Many countries in Asia already have strong digital health and insurance foundations. India’s Ayushman Bharat Digital Mission is building one of the world’s largest health data ecosystems, linking patients, facilities, and insurance platforms. Mongolia has launched a national e-health platform; the People’s Republic of China operates extensive insurance-linked electronic health records.

In the Republic of Korea, the Health Insurance Review and Assessment Service manages one of the world’s richest claims databases. Singapore’s National Electronic Health Record connects public and private providers, while Thailand’s Universal Coverage Scheme integrates digital financing and service delivery. In the Philippines, PhilHealth is expanding its e-claims and e-prescription platforms.

These systems provide ready-made entry points for generative AI. Instead of launching isolated pilots, countries can layer AI tools on existing infrastructure, using insurance data to identify health trends, automating compliance and reporting, or generating decision-support dashboards for frontline workers.

Generative AI’s value lies not only in its novelty but in its ability to make health systems faster, fairer, and more efficient. Yet it must be built on sound governance.

To assure the responsible integration of artificial intelligence in healthcare, countries can take several crucial steps. First, it is essential to define high-risk applications and ensure that clinical AI tools meet rigorous safety and ethical standards. Additionally, there must be transparency and explainability in how these algorithms operate and make decisions. It is also vital to maintain human oversight so that AI supports, rather than replaces, health professionals.

Investing in local data and workforce skills is necessary to create systems that reflect local needs and reduce bias. Finally, countries should leverage existing digital health platforms to scale these technologies responsibly and sustainably.

Health care has been slow to join the digital revolution. But generative AI offers a chance to catch up, and even leap ahead. Used responsibly, it can reduce paperwork, improve diagnosis, expand access, and strengthen trust in public health systems.

Countries will need to move from digital delay to digital leap. The challenge is not whether to use AI in health care, but how to use it safely, equitably, and for the greatest good.