Back to cases Case 05 · Dr. Sintomas · 2017

AI that recognizes
human emotions.

How I designed the UX strategy for AI systems that recognize emotions and behavior in healthcare triage — in 2017, before the generative AI wave.

Client
Dr. Sintomas
Period
2017
My role
UX Strategist
Impact
3 medical partnerships
ai
Context

Affective AI in healthcare, 6 years before the hype.

Dr. Sintomas was a healthtech startup using AI to identify medical conditions and guide treatment. The differentiator: AI systems that recognize emotions and human behavior to make triage more accurate and humane.

In 2017, this was academic research, not a commercial product. My role: define the end-to-end UX strategy, build flows for different user segments, and ensure the system respected patient privacy in a pre-LGPD context.

Timeline · AI in product
2017 Dr. Sintomas Affective AI applied to product
6 years before
2023 ChatGPT Generative AI goes mainstream
My role

Strategy, research and delivery in 5 months.

01
Strategy
End-to-end UX translating business goals into scalable flows. Partnered with CEO and COO on roadmap, go-to-market strategy, and investor presentations.
02
Research with vulnerable users
In-person usability testing with patients experiencing real symptoms. Tests revealed that emotion recognition needed to be progressive, not immediate.
03
Delivery
Design System + 3 custom flows by segment. A/B testing reduced usability issues by 85%. All delivered in 5 months.
The core challenge

When the system knows too much.

Testing with patients revealed a fundamental tension: users in healthcare contexts became uncomfortable when the system "knew" too much too soon. The AI's accuracy was real, but the perception of surveillance triggered abandonment.

The solution: progressive triage. Simple questions first, emotion and voice recognition as a secondary confirmation layer. The system learned gradually, and users felt in control at every step.

In parallel, we operated with privacy-by-design as a product constraint from day one: every UX decision considered which data was necessary, how it was communicated, and what would never be collected. This was 2017, pre-LGPD, post-GDPR.

"The system understood the patient's emotional state before they finished describing their symptoms. Privacy had to be as careful as precision."

Impact

Product working, partnerships won.

85%
reduction in usability issues through prototyping and A/B testing.
3
partnerships with doctors and 1 health plan actively evaluating the ML solution.
3
custom flows by segment delivered in 5 months.
Affective AI Healthtech UX Strategy Privacy-by-design A/B Testing Design System
Learnings

What I take from this case.

· Users in healthcare contexts are more sensitive to the perception of surveillance. Design must communicate control at every step, not just in the privacy policy.

· Progressive AI: gradually revealing what the system perceives reduces discomfort without losing precision. The timing of "I know" matters as much as the data itself.

· Operating in AI before the hype means navigating without established references. Which makes the work harder and, also, more original.

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