How to Build Trust in Clinical AI
AI can work in a demo and still be difficult to trust inside a real clinical workflow.
Once AI starts influencing clinical decisions, collecting patient information, or automating parts of a care workflow, “the model works” is no longer enough. Teams need to understand what happened, what was expected to happen, when a human should step in, and what evidence exists to support those decisions.
In this session, Xmartlabs Engineering leaders Marcos Soto and Daniel Baena walk through three areas that matter most when bringing AI into a clinical workflow:
Traceability: Can you reconstruct what happened and why?
Evaluation: Can you compare actual behavior against what was expected and identify when something went wrong?
Governance: Can you show which controls were applied, where human review is required, and produce evidence that can be audited?
We will walk through examples from work Xmartlabs has done around clinical intake, AI evaluation, architecture, and compliance to show how these ideas apply in real systems.
If your team already has an AI feature, prototype, or workflow in development, this session will help you think about what needs to be in place before you can confidently rely on it in a clinical environment.
Who should join
This session is designed for CTOs, Product Engineering leaders, Heads of Engineering, technical founders, AI leads, and Product leaders at HealthTech companies already exploring or building AI into clinical workflows.
The conversation will be technical enough to be useful to engineering teams, but focused on system design and decision-making rather than code.
We will leave time for Q&A, so bring a workflow, use case, or technical challenge your team is currently working through.
About Xmartlabs: is a software development studio offering nearshore development support, fractional CTO services, and embedded engineering teams. We help clinics, digital health, and medtech companies build and launch AI-native, thoughtfully designed products.