AI is moving quickly — more quickly, in fact, than many healthcare organizations are comfortable with.
As someone responsible for building and shipping product, I feel that tension every day. There’s pressure to move fast, and there’s the responsibility to ensure what we release is accurate, secure, and worthy of the environments it operates in.
At Authenticx, trust isn’t something we talk about at the end of a launch cycle. It shapes how we design from the start.
The Real Question to Ask
One of the first questions I ask when evaluating a new AI capability isn’t just, “What can this model do?”
It’s, “How can we build this in a way that we trust — and that our customers can trust, too?”
Sometimes that means changing the technology we use. Other times, it means strengthening the rubric that generates the labels our models are trained on.
But always, it means being able to clearly explain how the model was built, what data it relies on, and where its boundaries are without hiding behind footnotes or caveats.
Healthcare AI Cannot Be Generic
Healthcare conversations are complex. They’re emotional, regulated, and consequential.
That’s why we don’t treat healthcare AI as a generic language problem. Our models are built on a healthcare-specific corpus of conversation data, developed over time and labeled by a dedicated team of highly trained analysts who generate the data used to train and test our models.
For us, it’s less about chasing whatever model is trending and more about grounding the system in the realities of contact centers supporting patients and providers. That foundation ensures our models are ready to use for healthcare organizations — and able to deliver insights that drive immediate business impact.
In this space, accuracy isn’t just technical performance. It’s contextual understanding.
AI + Human Oversight (By Design)
AI should never operate independently of human judgment.
Human expertise is embedded throughout how our models are built and maintained. Analysts label and review data. Teams conduct quality assurance. Data scientists monitor performance as use cases evolve. When models drift, we catch it.
AI surfaces patterns at scale. Humans provide context, accountability, and oversight. That balance is built into every step of our development and design process.
No One-Size-Fits-All
Responsible AI in healthcare means recognizing that no two organizations operate the same way.
We get customers started quickly with out-of-the-box capabilities built around common healthcare use cases. But we also design for flexibility. In high-stakes compliance scenarios, we fine-tune, configure, and refine to drive the most effective outcomes.
It also means choosing the right technique for the problem at hand — from traditional machine learning to generative AI scoring to AI assistants that enable exploration and action. The goal isn’t to push a single method, but to deliver the outcome the customer is trying to achieve.
Governance Isn’t a Feature; It’s a Requirement
Bias monitoring, security safeguards, HIPAA-aligned de-identification, configurable redaction, data retention controls — these aren’t edge cases for us. They’re baseline product requirements.
When customers provide protected health information, we process it under appropriate agreements. Data retention follows contractual terms. Export and deletion are supported.
These fundamentals make everything else possible.
The Standard We Hold Ourselves To
AI will continue to evolve. New capabilities will emerge. And we’re excited about what we can build as models improve and techniques mature.
But in healthcare, adoption alone isn’t progress.
At Authenticx, we’ve built a system for developing and managing AI that we’re proud of — and confident discussing with our clients.
For us, the real standard is simple:
Are we building systems we trust — and that our customers can trust, too?
Sarah Purvlicis is the Manager of Product & Design at Authenticx, helping define what responsible AI looks like in healthcare.
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