While healthcare has always been tethered to stringent compliance, privacy, and safety protocols, artificial intelligence (AI) has introduced a greater need for governance. As a result, policymakers around the world are introducing tighter oversight and enforcement, forcing health systems, providers, and life sciences companies to shift from reactive to proactive approaches to AI governance.
In this new environment, regulatory compliance isn’t just another item on the to-do list—it’s a defining element of healthcare innovation. It can be treated as a burdensome box to check, or it can be embraced as a strategic opportunity to develop AI solutions that are safer, more equitable, and more aligned with the mission of care.
It’s about More than Avoiding Penalties
Compliance isn’t just about avoiding fines or legal and public backlash—it’s about building AI systems that clinicians, patients, and regulators can trust. AI that supports diagnosis, triage, or treatment recommendations must not only be accurate, but explainable, unbiased, and secure. Organizations that treat compliance as a foundation for ethical and effective AI will be better positioned to benefit from its value.
It’s getting there that’s the tricky part. The entire healthcare industry is grappling with how to build compliant AI systems in the face of changing laws, evolving ethical standards, and rapidly advancing technology. In 2024, lawmakers in 45 states introduced 635 AI-related bills, of which 99 became laws. In the healthcare industry alone, there were an additional 13 guidance frameworks. That’s a lot to be aware of, let alone remain compliant with.
Why the Push for Compliance Now?
These new guidelines and legislation are merited. Large language models (LLMs) and generative AI are raising new concerns across clinical and operational workflows—from hallucinated outputs in clinical decision support tools, to biased datasets that can exacerbate health disparities. These risks are not just theoretical. Flawed algorithms have already caused real-world harm, from biased diagnostic models to flawed clinical trial recruitment tools.
Healthcare AI has unique vulnerabilities, and regulators are taking notice. High-profile cases of algorithmic bias and privacy violations have pushed governments to act. Legislation such as the EU AI Act, the U.S. Executive Order on AI, and emerging FDA guidance all aim to ensure AI used in healthcare is transparent, accountable, and safe. These frameworks are necessary not only to protect patients but also to sustain public trust in both receiving and administering AI-assisted care.
AI for Navigating Compliance
Far from hindering progress, regulation can drive innovation—especially when compliance is embedded early in the development process. In healthcare, where the stakes are high and the margin for error is slim, compliant AI is a competitive advantage. And increasingly, healthcare organizations are turning to AI itself to manage this complex landscape.
In fact, a flurry of AI tools have emerged in response to monitor and interpret evolving regulations, flag risks in clinical algorithms, and help maintain audit trails required for compliance with HIPAA, GDPR, and other health data regulations. Additionally, communities like the Coalition for Health AI (CHAI™) have assembled, bringing together a diverse group of experts in the field to collaborate on the development, evaluation, and appropriate use of AI in healthcare.
Compliance-Driven Innovation in Action
Consider predictive models used to identify patients at risk of hospital readmission. Without rigorous governance, such models may perform differently across patient populations—introducing disparities based on race, age, or socioeconomic factors. However, with a compliance-first approach, data scientists and clinicians can jointly design validation protocols that evaluate model fairness and safety across different cohorts.
AI can help generate synthetic patient profiles and continuously monitor performance in production environments. These practices not only meet regulatory expectations but also improve clinical outcomes by making the model more inclusive and robust. And clinical risk models are just one application. There are many more cases where achieving high compliance standards is more beneficial than it is restrictive.
Additionally, AI tools designed with compliance in mind are less likely to face costly redesigns, litigation, or delays in FDA clearance. They’re also more explainable—meeting demands from domain experts who need to understand how decisions are made, not just what they are. This transparency can build clinician confidence and ultimately drive adoption. Although it’s a slow burn, it seems that we’re headed in the right direction, according to a recent American Medical Association (AMA) survey, which found not only are more physicians using AI, but reported a more positive sentiment toward the technology than in years past.
Compliance shouldn’t be seen as just a box to check. While legally healthcare organizations have no choice but to align their AI practices, leaders can also choose to view regulatory compliance through a different lens. It can also be a strategic enabler that helps healthcare organizations build AI that’s ethical, accurate, and actually gives users a competitive edge. And that’s where real innovation happens.
David Talby
David Talby is CEO for Pacific AI and CTO for John Snow Labs.
