SkinLife

“AI-augmented dermatologists providing skin care anytime, anywhere.”

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Inspiration

During the 2020 COVID lockdown, I struggled with severe acne. Living in a small rural area, there were no dermatologists nearby — the nearest city was almost 1.5 hours away. As my acne worsened, so did my self-esteem and mental health. That personal experience made me realize: Many Malaysians silently face the same problem — limited access to timely, expert skin diagnoses and treatments.

That’s when the inspiration for SkinLife was born — to build an AI-powered teledermatology platform that bridges the gap between technology and healthcare, especially for underserved communities.

What it does

SkinLife is a mobile-first teledermatology Next.js web app that augments dermatologists with AI initial symptom triage and a RAG chatbot-like assistant that helps retrieve medical info and files to better serve patients.

It also enables users/patients to:

  1. Upload skin images for instant AI symptom analysis/triage using multimodal large language models.
  2. Have AI analysis further sent to a dermatologist for professional review and diagnosis.
  3. Receive dermatologist-reviewed diagnoses and prescriptions with personalized health reports.
  4. Get automated prescription scheduling and reminders integrated with their calendar.
  5. Automatically get mapped to the available & most suitable dermatologist based on their skin disease, location and etc.

In short, SkinLife bridges the gap between technology and healthcare, delivering accessible, intelligent dermatology care for all.

How we built it

We combined multiple technologies to make SkinLife fast, intelligent, and scalable.

  • Full Stack: Built with Next.js (mobile-first responsive design), TypeScript, Tailwind CSS
  • Database & Auth: Supabase and PostgreSQL for user management, image storage, and role-based access.
  • AI Models: Image analysis via OpenAI, LangChain for context-aware chatbot.

The architecture supports AI triage, human verification, and data analytics — forming a full digital dermatology pipeline.

Challenges we ran into

  • Data privacy: Designing secure storage for medical images and reports compliant with healthcare standards.
  • Explainability: Making AI explanations simple and medically accurate for users.

Accomplishments that we're proud of

  • Developed a fully functional prototype within days.
  • Partner-ready design for clinics and skin health centers.
  • Delivered natural language explanations for skin conditions via AI.
  • Designed an inclusive system ready to serve both urban and rural communities.
  • Built a scalable model architecture that can expand to other telehealth fields such as eye care and dental.

What we learned

  • Integrating multimodal AI (image + language) can greatly improve patient understanding and trust.
  • User experience and data privacy are just as crucial as AI accuracy in healthcare apps.
  • Effective collaboration between technical and medical teams ensures credibility and adoption.
  • Even simple tools, when combined thoughtfully, can bring real-world healthcare impact.

What's next for SkinLife

  • Clinical pilot with health centres and local dermatologists.
  • Expand to regional markets (Singapore, Indonesia) with localized models.
  • Partner with Ministry of Health and telehealth providers (TeleMe, DoctorOnCall) for integration.
  • Introduce subscription plans and AI-powered skin journals for long-term users.
  • Continue training on Malaysian skin datasets to improve accuracy and inclusiveness.

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