For years, AI diagnostics have been evaluated primarily through the lens of algorithm performance, market authorization and reimbursement planning. Increasingly, however, value is no longer created by the algorithm alone. It is created by the ability to integrate AI into a reproducible diagnostic ecosystem that demonstrates clinical utility, economic value, and sustainable reimbursement.
CMS’s proposed introduction of Software as a Medical Service (SaMS) within the CY 2027 Medicare Physician Fee Schedule signals that clinical software is increasingly being viewed as a payment-policy challenge, not simply a medical device regulatory one. While this remains a proposed rule rather than an established reimbursement pathway, it reflects an important shift in thinking.
The next phase of competition will not be won on algorithms.
It will be won by the ability to translate technology into evidence backed, implementable, reimbursable value.
Many AI companies still develop regulatory, clinical, reimbursement, and commercial strategies as separate activities. Increasingly, that approach creates risk. Regulators, providers, payers, investors, and healthcare systems will expect a connected strategy that demonstrates not only analytical performance, but also reproducibility, interoperability, workflow integration, and measurable clinical and economic impact.
Recent market developments point in the same direction:
Leica Biosystems’ planned acquisition of StatLab illustrates how the industry is expanding beyond scanners and AI toward greater control of the diagnostic workflow. Consistent pre-analytics, standardized laboratory processes, and high quality slide preparation are foundational to reproducible AI performance. While unrelated to CMS reimbursement policy, the transaction reflects the same broader trend: value is created across the entire diagnostic pathway, not at a single point within it.
For executive teams, this changes the strategic question.
The objective is no longer simply obtaining regulatory market authorization.
It is ensuring that technology, implementation, interoperability, clinical utility, economic value, and reimbursement are designed as one integrated strategy rather than independent activities.
The question is no longer “Can this software be authorized?”
It is “Can its value be consistently demonstrated, implemented, reimbursed, and sustained across the healthcare ecosystem?”
At SolarisRTC, we refer to this integrated approach as Evidence Architecture, the strategic discipline of aligning scientific, clinical, operational, economic, and reimbursement evidence so AI diagnostics can move from authorization to sustained adoption and value.