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Sarah Oluwatosin

Sarah is a Senior Product Designer with expertise in health tech, fintech and SaaS design. She also has skillset in web design and chatbot design

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chm-02-min
AI | Women's Health
2 min read

Building a Women’s Health Chatbot with Replit

chm-02-min
May 22, 2025

Building a Women’s Health Chatbot with Replit

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Over the weekend, I designed and prototyped an idea I had.

The idea?
To create a safe, judgment-free digital space where women can ask health questions, get reliable answers, and feel seen, especially in places where access to care is limited or stigma makes it hard to speak up.

What I built (and how): A web app with login and a built-in women’s health chatbot.

Designed a clean interface with Figma
• Developed on Replit for speed and flexibility
• Used Firebase for authentication and secure login
• Integrated OpenAI for conversational AI (trained with curated women’s health content for accuracy)

The result?

A functional prototype that shows what’s possible when you combine design, tech, and empathy in women’s health.

This isn’t a live product yet. But it’s a starting point to explore how tech can support women’s health with empathy, privacy, and accessibility in mind.
While building the prototype, I focused on:
• Understanding kind of questions women struggle to ask
• Designing flows that feel calm, respectful, and intuitive
• Testing tone, logic, and clarity in chatbot conversations (still needs improvement)
• Thinking about how this might work in culturally sensitive contexts

If this were to go live, here are a few things that would be non-negotiable:
✅ Data Privacy & Compliance
• Full HIPAA compliance (US): This ensures that protected health information is encrypted and secure
• GDPR compliance (UK/EU): This ensures clear consent, the right to be forgotten, and transparent data handling
• Minimal data collection: only what’s necessary, nothing more

✅ Medical Accuracy
• All chatbot responses would need review by certified professionals
• Clear disclaimers that it’s not a substitute for professional care — more like a first step toward it

✅ Bias & Inclusion
• Training data and design must reflect diverse health experiences, inclusive of different cultures, identities, and backgrounds
• Language must be plain, neutral, and stigma-free

✅ Accessibility
• Support for screen readers, voice input, and low-bandwidth environments
• Additionally, multilingual support would be important for indigenous or underserved communities

This prototype is just the beginning, but I’m excited about where it could lead.
👉 Check out the prototype here: https://lnkd.in/eVTM9fMB

If you’re working in FemTech, MedTech, or digital health, I’m happy to have to connect, my inbox is open.

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