In the early days of digital transformation, APIs were the universal answer. Need to connect two systems? Build an API. Need to share data across platforms? Expose an API. In many industries, this was enough. Finance, logistics, and retail all benefited from an “API-first” approach.
But when it comes to healthcare — and especially to medical AI — APIs alone are not enough.
The Limits of APIs in Healthcare
APIs can move data. What they cannot do is solve the deeper legal, ethical, and security requirements that govern health information. Medical data is classified as a special category in both EU and US law, subject to layers of protections far beyond technical interoperability.
Three problems quickly emerge:
- Lawful Basis Every use of health data requires a specific legal justification — consent, contract, or statutory allowance. APIs don’t handle consent management or proof of lawful access.
- Secure Environments Regulations like the EU’s EHDS require data to remain inside Secure Processing Environments (SPEs). An API call that extracts data to an external cloud is already non-compliant.
- Audit and Accountability Healthcare regulators demand full traceability: who accessed what data, for what purpose, under which consent. APIs provide a technical pathway, but not the governance, audit, and documentation.
The Rise of Secure Processing Environments
Europe is now formalizing this reality. Under the European Health Data Space (EHDS), each member state must establish Health Data Access Bodies and connected Secure Processing Environments by 2027–2029.
These SPEs are not just technical servers — they are legal and operational frameworks. They combine:
- Verified data access requests
- Consent and contractual controls
- Secure, in-place computation without exporting raw data
- Full logging and audit trails
Simply put: SPEs are where the future of medical AI will run.
Building Bridges, Not Just Endpoints
The challenge — and opportunity — is to build bridges between data holders and AI developers inside these environments. That means more than just technical connectors. It requires:
- Compliance-first design — aligning with MDR, FDA SaMD, and the EU AI Act from day one.
- Modular deployment — allowing approved AI modules to plug into SPEs without re-certifying the entire system.
- Shared value models — ensuring hospitals and data providers benefit financially, while developers can scale.
APIs may still play a role as technical components. But without lawful access frameworks and secure execution environments, they are only half a bridge.
Why This Matters Now
The timing could not be more critical:
- EHDS is live. Member states are already preparing their SPEs.
- MDR is mature. Notified Bodies have established clear review processes for medical AI.
- The AI Act is coming. From 2026, high-risk healthcare AI faces new obligations.
Together, these forces make compliance not a barrier, but a competitive edge. Early movers who align with lawful access models will become the reference cases — and the tollgates — for the next generation of medical AI.
Conclusion
APIs solved yesterday’s problems of interoperability. Today’s challenge is different: lawful, secure, and auditable access to medical data for AI innovation.
That requires a new kind of bridge — one that spans not just technical systems, but also legal, ethical, and business domains. Those who build and cross that bridge first will shape the future of healthcare AI.