Companies are incentivized to move quickly and generate evidence over time. Payers operate differently. Before committing to coverage, they typically require robust evidence demonstrating both clinical efficacy and cost-effectiveness at scale. The gap between those timelines is where reimbursement decisions often stall.
Assessment frameworks are still catching up as well.
Health Technology Assessment bodies across many markets continue to refine how AI-enabled tools should be evaluated. While initiatives such as the EU AI Act and national digital health assessment programs represent progress, the playbook is still evolving.
Clinical recognition creates another hurdle.
Payers are far more likely to reimburse AI-based medical devices when they are incorporated into clinical guidelines and recognized as part of the standard of care. Without that endorsement, even a well-evidenced tool can struggle to achieve meaningful uptake.
Evidence is not the same as adoption
This is where many AI diagnostic companies quietly go wrong. Reimbursement is often treated as a clinical challenge: generate enough evidence, demonstrate sufficient accuracy, and coverage will follow.
In practice, it rarely works that way.
Evidence is necessary, but it is rarely sufficient.
The organizations that have successfully broken through have done more than accumulate data. The introduction of a permanent CPT code for AI-based diabetic retinopathy screening in the US, the national reimbursement for an AI brain MRI tool in South Korea, and HeartFlow's inclusion in the NHS MedTech Funding Mandate were not simply the natural outcome of strong science.
They were commercial and market-access achievements, built on compelling evidence but also on strategic engagement with policymakers, payers, and other decision-makers.
The same principle applies to clinical guideline inclusion.
NICE in the UK recommends AI technologies for fracture detection in urgent care, while Germany's national lung cancer screening program requires the use of CE-marked AI diagnostic software.
Neither development was a passive reward for scientific progress. Both required sustained advocacy and a clear articulation of value to the people responsible for shaping policy and clinical practice.
Value does not speak for itself. It must be translated into terms decision makers can understand, evaluate, and support.
Evidence may open the door, but what happens next is ultimately a commercial decision.
Funding as a commercial bridge
National reimbursement is not the only route to market. Healthcare providers can often be persuaded to invest in AI-enabled diagnostic tools directly, even without a dedicated reimbursement code.
The value proposition is typically operational rather than reimbursement driven. AI can streamline diagnostic workflows, improve resource allocation, reduce clinician workload, and support service differentiation.
Some companies are also beginning to experiment with patient co-pay models for AI-assisted screening and diagnostic services.
These approaches can be effective, particularly in the early stages of market development.
However, they are best viewed as a bridge rather than a destination.
Without a path to broader reimbursement, companies risk becoming trapped in a commercial model that generates revenue but fails to build the evidence base, policy support, and stakeholder alignment needed for sustainable national coverage.
The real test
Payers are likely to come around. The momentum behind AI-enabled diagnostics is real; policy signals are increasingly supportive, and early reimbursement successes are beginning to establish a roadmap for the wider market.
But the companies that succeed in turning breakthrough diagnostics into reimbursed, scalable solutions will not get there on clinical merit alone.
They will be the ones that understand early what their technology is worth, who captures the value it creates, and how to articulate that value in terms that payers, policymakers, and healthcare systems can act on.
Over time, payer skepticism can be overcome with stronger evidence, clearer outcomes, and a compelling commercial case.
The challenge is that none of those things happen automatically.
For many AI diagnostics companies, the harder challenge is no longer building the algorithm. It is proving value in a way healthcare systems can trust, adopt, and sustain.
In that sense, AI diagnostics are not unique. They are simply one of the clearest examples of a broader reality emerging across industries.
Innovation creates potential.
Value is only realized when others recognize it.