Breast Cancer AI Portal

Breast Cancer AI Portal leverages Google Gemini 3 to transform breast cancer trial data into real‑time, actionable insights. All patient data shown is fictitious and for demonstration only.

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Inspiration

The idea for the Breast Cancer AI Portal was inspired by the need to simplify oncology workflows. Breast cancer care involves complex trial data, pathology reports, and patient histories. I wanted to build a tool that could transform this complexity into clear, actionable insights using AI, making it easier for clinicians and researchers to focus on patient outcomes.

What it does

BrCa AI Portal leverages the latest Google Gemini 3 models to generate patient‑specific case summaries, answer clinical questions in real time, and provide an intuitive interface for trial management. It acts as a clinical co‑pilot, helping oncologists quickly interpret tumor metrics, staging, and pathology data.

How I built it

  • Frontend – Designed a clean, user‑friendly interface for trial patient navigation.
  • Backend – Integrated secure endpoints to connect with Google Gemini.
  • AI Engine – Built a case analysis module that transforms structured trial data into summaries and Q&A.
  • Validation – Used deterministic parsing for tumor dimensions, e.g.: Tumor Volume = 6.0 x 3.0 x 2.0 = 36.0 This ensured reproducibility and accuracy in outputs.

Challenges I ran into

  • Data consistency: Avoiding duplicate or conflicting trial records.
  • Integration hurdles: Configuring Gemini endpoints securely under hackathon time constraints.
  • Communication gap: Translating AI outputs into language clinicians can trust.
  • Time pressure: Balancing innovation with rapid prototyping.

Accomplishments that I am proud of

  • Successfully integrated Google Gemini into a healthcare‑focused workflow.
  • Built a working prototype that delivers real‑time case summaries and Q&A.
  • Designed a portal that is both technically robust and clinically intuitive.
  • Ensured transparency by clearly stating that all patient data is fictitious and for demonstration only.

What I learned

  • How to architect AI solutions that balance technical innovation with clinical usability.
  • The importance of data privacy and reproducibility in healthcare projects.
  • That hackathon projects thrive when combining clarity, trust, and usability alongside technical depth.

What's next for BrCa AI Portal

  • Expand integration with electronic medical records (EMRs) for real‑world deployment.
  • Enhance the AI engine with multi‑modal data support (images, genomics, clinical notes).
  • Collaborate with healthcare institutions to pilot the portal in clinical trials.
  • Scale the architecture for enterprise‑level oncology platforms.

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