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Artificial intelligence-enabled medical technologies are emerging as a powerful force in modern health care — not only relieving documentation burdens and staffing shortages but also unlocking a new wave of competitive advantage.
Clinics and health care systems that implement ambient AI documentation tools stand to gain significant operational efficiency, enhanced patient engagement and faster throughput. For many institutions, these gains are catalysts for revenue growth, market leadership and next-generation delivery of care.
These tools are reshaping the regulatory landscape where AI in health care compliance is no longer optional but integral to sustainable growth.
But with great promise comes great risk. How can organizations move into the AI era, embracing innovation and scale while remaining grounded in compliance and reputational trust? Beneath the allure of AI-driven transformation lies a minefield of regulatory exposures — especially in health care, where cross-border data flows and the principle of purpose limitation pose complex challenges.
As sensitive health data moves from exam rooms to cloud-based models managed by third-party vendors — often outside national jurisdictions — health-care institutions may be exposing themselves to costly legal and reputational fallout.
The landscape: Evolving AI capabilities and data boundaries
Most AI-enabled medical technologies function by recording clinical encounters; they then process the audio or visual files through cloud-based large language models or uploads. These models are often owned or operated by vendors in different jurisdictions than the health care provider or patient.
In many cases, data leaves the clinical environment and enters global ecosystems governed by fragmented health care data privacy laws, limited transparency and minimal enforceability.
While global AI adoption is accelerating, regulatory frameworks like the U.S. Health Insurance Portability and Accountability Act have long maintained that unconsented or unauthorized use of data is unlawful. The issue is not the absence of rules, but the failure to operationalize them early enough in the AI development lifecycle. As tools originally designed for administrative relief begin to expand into diagnostics and treatment suggestions, both vendors and regulators must contend with the evolving legal implications of ambient AI.
The challenge: Innovation outpacing responsible use
At the center of this challenge is the principle of purpose limitation. Under privacy regulations — such as HIPAA, the EU General Data Protection Regulation, Canada's Personal Information Protection and Electronic Documents Act, United Arab Emirates' and Saudi Arabia's Personal Data Protection Law, and Brazil's General Data Protection Law — personal data must only be used for explicitly defined purposes at the time of collection.
Yet, data collected for clinical documentation is repurposed in many AI-enabled medical technology implementations — often without knowledge or consent — to train, refine scale AI models or enhance medical predictions with greater accuracy for enhanced patient diagnosis. In some cases, historical data collected under outdated authorizations is pulled into modern workflows without proper revalidation.
These issues are especially acute for companies working at the intersection of HIPAA and AI, where historical consents often fall short of modern AI use cases.
This type of reuse raises serious legal and ethical concerns. Patients and providers are rarely aware data is being transferred internationally or used in ways that extend far beyond their initial expectations. Even when deidentification is claimed as a safeguard, modern reidentification techniques — and the contextual richness of health data — render those assurances increasingly hollow.
As AI-enabled medical technologies evolve toward diagnostics and automated clinical recommendations, the risk landscape expands — introducing potential for biased outputs, discriminatory insights and regulatory misalignment if privacy safeguards aren't embedded from the start. As functionality increases, so does the risk of biased outputs, discriminatory impacts and data use that stretches or breaks existing regulatory frameworks.
What must be engineered into medical AI now
Certain actions are essential to realign AI-enabled medical technology deployment with legal obligations and patient rights.
Consent/authorization acquisition. Companies developing or deploying AI-powered medical software and devices must ensure appropriate HIPAA authorizations and similar privacy consents have been obtained — either directly by the provider or, when collected by health care professionals or medical clinics, with explicit notice to the patient that their data will be shared with the software or device provider. Most vendors likely lack this foundation, necessitating a retrospective compliance strategy.
Given the sensitivity of health data and the specificity demanded by modern privacy laws, opt-outs are insufficient. Blanket or implied consent does not satisfy the legal standards for informed, specific and voluntary authorization. This is not a matter of future-proofing — it's an obligation from the beginning.
Notional segregation of restricted data. Where datasets cannot be lawfully consented to — or where international transfers would violate local privacy law — those datasets must be actively segregated out of AI-training pipelines. Vendors must identify, isolate and remove data that is non-compliant or jurisdictionally restricted. Assuming workarounds or applying a one-size-fits-all logic across global datasets is no longer defensible.
Contractual transparency. Health-care providers must have full visibility into how patient data is processed, stored, reused and shared. Contracts must include detailed terms around downstream use, subcontractor access, model training rights, retention policies and remediation protocols.
Today, most health-care AI vendor contracts lack this level of specificity, leaving accountability gaps that undermine both patient trust and institutional integrity.
Mandatory privacy impact assessments and privacy by design. No AI-enabled medical technology should be deployed without a comprehensive PIA that explicitly addresses reidentification risk, algorithmic bias and downstream data use.
In line with emerging medical AI regulatory strategy expectations, privacy by design must be embedded not just into product engineering, but into procurement, contracting, and model governance.
Maintain accurate record of processing activities. ROPAs should reflect all AI-related data flows, particularly where health data is processed or transferred across borders. This record is a critical compliance artifact under the GDPR and other emerging laws.
AI-enabled medical technologies are indeed a timely innovation — poised to transform workflows and revenue models alike. But the benefits of AI adoption cannot be decoupled from the responsibilities of data stewardship. Strategic investment in health-care data privacy compliance isn't a constraint on growth, it's the infrastructure that makes scale sustainable.
In a climate of rising enforcement and growing public awareness, the risks of sidestepping data protection aren't abstract. They're operational, reputational and — increasingly — existential. AI may be optional. Compliance is not.
The opportunity to get it right
The privacy field rarely gets the chance to shape compliance at the point of design. But today, with AI still in the early stages of integration into health-care workflows, organizations have a critical window of opportunity: to build privacy into the foundation of their systems, rather than bolt it on after the fact. This is a pivotal moment.
Organizations that act now can establish trust, regulatory resilience and long-term scalability by doing things from the start. Those that delay will find themselves years deep into AI deployment, retrofitting consents, rewriting contracts and scrambling to explain opaque data flows to regulators and the public.
Privacy, when addressed early, is a multiplier — not a roadblock. Right now, the industry has a rare chance to get it right — and stay right. We are at a turning point.
The healthcare industry has the rare opportunity to treat privacy not as a legal afterthought, but as a core component of innovation. As privacy evolved from static policies to dynamic frameworks embedded into technology, the call is clear: now is the time to build responsibly.
Manika Gupta is founder and partner, and Adilson Braga Jr. is business development associate at Privacy Evolved.
