Vlad Panin, CEO at iFrame, is a corporate lawyer, healthcare administration expert and scientist.
For executives racing to lead in healthcare AI, I see the European Union, the United States and China as the leading regulatory paradigms for the next decade. Europe’s careful, risk-based legislation, the U.S.’s sector-specific, enforcement-backed approach and China’s state-led, data-sovereignty model can each help us define how AI platforms clear markets, manage compliance costs and mitigate risk.
Europe
Recently, the EU approved the landmark Artificial Intelligence Act, classifying many healthcare AI applications as high‑risk and mandating conformity assessments. As noted by Jones Day, violators can face significant penalties “including the greater of either up to €35 million or 7 % of their global annual turnover.”
At the same time, McKinsey and other industry sources project that AI could yield hundreds of billions in savings, with global estimates sometimes reaching between $270 and $320 billion in administrative cost reductions.
Complementing this, the European regulatory framework, including the European Health Data Space (EHDS) and GDPR, enforces strict consent, transparency and data‑protection standards. While specific global savings figures vary, analyses like those from McKinsey highlight significant potential for AI‑driven automation to reduce administrative burdens. Investment initiatives, such as the Horizon Europe programme, allocates €95.5 billion for research and innovation through 2027.
United States
In contrast, the United States operates under a more regulatory patchwork, which includes the FDA’s guidance on AI/ML in software as a medical device (SaMD), recent HHS HIPAA updates and proposed state-level measures such as California’s AI disclosure requirements (i.e., AB-3030) and New York’s algorithmic bias proposals. These all help create a landscape where legal actions have already resulted in settlements.
For instance, a promiment case is the Cigna PxDx algorithm case, which alleges “Cigna used PxDx to automatically review and deny over 300,000 claims.” This kind of automated decision-making raises concerns not only about fairness and transparency but also about the broader administrative inefficiencies in the healthcare system. In fact, U.S. administrative spending reached $950 billion, or 15% to 25% of total health expenditures in 2019.
Med.Report suggests AI coding solutions offer potential for significant denial reductions and throughput improvements. Accenture forecasts that key AI applications could “create $150 billion in annual savings for the US healthcare economy by 2026.” Yet, according to analyses like those from Arcadia, implementation cost and regulatory uncertainty are frequently cited by healthcare leaders as chief obstacles to AI adoption.
China
Under “Healthy China 2030” and the 14th Five‑Year Plan, AI development and adoption in healthcare are prioritized, accelerating rollout in major hospitals and supported by national efforts to develop AI technical standards.
This rapid integration of AI into clinical settings is taking place alongside stringent data governance. The Personal Information Protection Law (PIPL) mandates data localization, separate consent for processing sensitive personal information and periodic compliance audits for data handlers. At Shanghai’s Ruijin Hospital, the RuiPath AI pathology model—developed with Huawei and SenseTime—now delivers preliminary pathology reads in seconds, all within PIPL’s security framework.
Such advancements are unfolding within a booming healthcare market. According to a Frost & Sullivan report, China’s healthcare market is projected to reach approximately $1.95 trillion by 2025, growing at nearly 10% annually.
Three Playbooks
So, what’s the playbook for taking healthcare AI global? It means understanding three very different game plans. Europe plays it safe. Get your AI approved before launch. Prove it meets strict risk and data rules (AI Act, GDPR). Why? Partially because mistakes can bring massive fines. So, caution comes first.
The U.S. acts differently. You might launch faster here. Rules often target specific sectors, like FDA for devices or HIPAA for data security. But be ready. Problems surface after market entry, often through costly lawsuits (like the Cigna case) or hefty HIPAA penalties. Enforcement follows deployment.
China moves fast, led by the state. Scale is the goal—and under national plans. AI gets rolled out particularly quickly in hospitals. But data must stay local, governed by strict PIPL rules. Control is paramount.
In my opinion, companies must focus on these core strategies to achieve success across global markets:
1. Build flexible tech. Design AI like building blocks. Swap modules easily for EU, U.S. or Chinese rules.
2. Design for transparency. Show your work. Make AI explainable, especially for EU and U.S. eyes.
3. Partner locally. Work hand-in-hand with EU-notified bodies, U.S. agencies like FDA/OCR and Chinese health authorities. Know the local players.
4. Prepare for risks. Set aside funds. Big EU fines, U.S. penalties and lawsuits are real possibilities. Plan for them.
For healthtech firms with global ambitions, building regulatory agility across these key markets is no longer optional—it’s fundamental to achieving scale and sustainable growth.
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