Naghmeh Nouri
The planning shift that connects regulatory clearance to reimbursement, adoption, and scalable lifecycle management.
AI/ML-enabled IVDs are increasingly reaching regulatory clearance, yet many still fail to reach patients at scale. The issue is not whether the product can satisfy regulatory requirements, but whether the development program was designed early enough to support reimbursement, clinical adoption, lifecycle governance and commercial scalability. Critical success factors were treated as downstream activities, an avoidable, costly mistake. The pattern: cleared but not reimbursed, authorized but not adopted, compliant but not scalable.
Regulatory Clearance Is No Longer the Hardest Problem to Solve
AI/ML-enabled IVDs now sit at the intersection of IVD regulation, Software as a Medical Device (SaMD) requirements, AI governance, cybersecurity, payer evidence standards, clinical workflow integration, and post-market lifecycle management. Each of these workstreams has design implications. None can be sequenced behind regulatory strategy without creating cost, delay, or avoidable rework later.
Regulatory approval is no longer the hardest problem to solve. The harder and more commercially decisive problem is whether evidence, reimbursement, and operational readiness were engineered in parallel with regulatory strategy from the concept stage, rather than sequenced behind it. Too many programs still organize around the approval milestone and treat every other workstream as downstream. That is how products reach clearance yet fail to reach patients.
Five Planning Gaps That Are Creating the Most Avoidable Downstream Cost
Across AI/ML-enabled IVD and companion diagnostic programs at multiple stages of development, the most common sources of late-stage cost and delay cluster around five areas:
- Regulatory misalignment. AI/ML-specific requirements addressed late rather than embedded from the start.
- AI model bias. Dataset governance treated as a technical matter rather than a regulatory and quality requirement.
- Algorithm change control. No predetermined change control programs (PCCP) or structured change control framework in place before development begins.
- Fragmented reimbursement. Payer evidence not built into clinical study design from the beginning.
- Operational readiness. Quality systems and cybersecurity controls sequenced behind, not built in parallel with regulatory and clinical work.
The white paper goes deep on each: what causes the risk, why it matters, and what leadership teams can do earlier. [DOWNLOAD LINK]
Two Regulatory Frameworks, Four Market Signals, One Operating Model Decision
For AI-enabled companion diagnostics regulatory complexity is no longer the only challenge. Fragmented planning is now a strategic risk.
FDA expectations, EU IVDR requirements and the EU AI Act each create a regulatory environment that requires coordinated execution across product development clinical evidence, quality system, risk management cybersecurity, data governance and lifecycle oversight. While each framework introduces distinct requirements, many of the underlying evidence and foundation are shared. Companies that recognize this early can build once and adapt efficiently. Companies that plan them sequentially often face avoidable remediations, duplicated efforts, and tighter timelines when it is least convenient.
At the same time, the market is redefining what is expected from a competitive diagnostic product. Liquid biopsy and genomic profiling, digital pathology, cloud-based analytics platforms, and real-world data integration are each reshaping what regulatory, evidence, and commercial planning needs to account for now.
Five Moves That Turn Regulatory Success into Market Success & Adoption
The manufacturers navigating this well share a consistent approach: they integrate regulatory, clinical, reimbursement, quality, and commercial planning from the beginning. The white paper outlines five specific moves leadership teams can take:
- Design for regulatory approval, reimbursement and adoption
- Integrate clinical evidence and real-world generation early
- Implement AI lifecycle governance as a strategic capability
- Develop strategic partnerships that accelerates validation and access
- Position product around clinical and economic value and beyond the test
None of these moves require a program overhaul. Each is considerably more achievable when it starts early.
To discuss where your AI/ML-enabled IVD program stands, visit veranex.com/contact/project-request.
About the author - Naghmeh Nouri served as Executive Director, Quality and Regulatory at Veranex.
