Article
How to Build an FDA-Ready Ultrasound AI Program
February 2, 2026 · 14 min read
A practical operating sequence for connecting intended use, protocol design, data quality, and defensible model claims.
Many teams begin by collecting as much data as possible and then search for a claim later. In regulated clinical products, that sequence usually creates expensive rework.
If intended use is vague, every downstream artifact becomes fragile. If intended use is precise, the whole program aligns faster.
Begin with claim architecture
A stronger approach is to define intended use, target population, and clinical context first. This gives a stable boundary for protocol design and model evaluation.
When claim architecture is explicit, the team can decide what evidence is required, what subgroup behavior must be analyzed, and which failure conditions must be monitored.
This early alignment also improves communication with clinicians, quality teams, and leadership because everyone references the same objective language.
Translate protocol into operational controls
Protocol documents are necessary, but operations succeed only when the protocol is translated into daily controls that teams can execute.
The quality model should define how data is captured, how deviation is triaged, and who owns corrective actions at each stage.
- Predefine acceptable acquisition ranges for depth, gain, and frequency.
- Track site-level deviations with clear ownership and response windows.
- Freeze subgroup definitions before final model evaluation begins.
- Maintain traceability from protocol decisions to model outputs.
- Log quality gate approvals with reviewer names and dates.
- Keep one shared evidence register for clinical, software, and ML teams.
Build evidence flow for submission and release
A frequent failure mode is to treat validation and release documentation as a final phase task. By that point, data and decisions are often hard to reconstruct.
Instead, teams should treat documentation as a continuous stream: each review cycle should produce traceable updates to risk records, protocol notes, and evaluation summaries.
This approach reduces review friction, improves audit readiness, and makes release decisions more transparent for both internal and external stakeholders.
Most importantly, it protects clinical trust by showing exactly why a model behavior is accepted, limited, or escalated.