This is a submission for the Google AI Studio Multimodal Challenge
I built a Women’s Health AI Web App that focuses on improving early breast cancer detection, overall women’s health awareness, and doctor efficiency.
The app solves two main problems:
For Doctors – It reduces workload by automatically triaging mammograms and other scans, generating annotated heatmaps, risk scores, BI-RADS classifications, and longitudinal comparisons. Doctors receive AI-prepared reports that they can manually review, saving significant time.
For Patients – It empowers women with personalized education and health guidance. The app generates simplified visual reports, self-exam teaching modules, cycle tracking, pregnancy visuals, nutrition annotation, and skin lesion checks.
This creates a dual experience:
Doctors get a technical clinical assistant that improves efficiency.
Patients get a personalized health companion that simplifies complex medical data.
Uploading a mammogram and receiving an annotated heatmap with BI-RADS score.
Patient-friendly report with plain-language explanation.
Doctor dashboard with Smart Triage Queue and longitudinal tumor tracking.
Teaching module showing self-breast exam steps generated as annotated visuals.
(If Gemini 2.5 Flash Image trial access is no longer active, include a demo video showcasing the AI-generated annotations, risk scoring, and education modules.)
🛠️ How I Used Google AI Studio
I leveraged Google AI Studio as the foundation for my app by integrating Gemini’s multimodal capabilities:
Text + Image Understanding → Mammogram, ultrasound, and X-ray scans are uploaded, and Gemini interprets the medical content, returning clinical risk scores and explanations.
Image Annotation → Gemini automatically highlights suspicious areas in scans (tumors, lesions, cysts, bone density loss).
Image Generation → Used for patient education (e.g., breast self-exam visuals, pregnancy growth diagrams, healthy vs unhealthy mole comparisons).
Multimodal Context Handling → The model takes into account both the image and patient history (age, symptoms, family risk factors) to generate personalized outputs.
This allowed me to build a system where Gemini is not just analyzing but also teaching, guiding, and supporting clinical workflows.
🌐 Multimodal Features
Here are the key multimodal features and why they matter:
Scan Upload + Annotation (Image → Text + Image)
Doctors and patients upload medical scans.
Gemini analyzes and returns annotated heatmaps, BI-RADS categories, and explanations.
Enhances accuracy, speed, and clarity for doctors.
Report Generation (Text → Dual Reports)
Clinical report (technical, BI-RADS, risk factors).
Patient report (plain language, visuals).
Enhances communication between doctors and patients.
Teaching Modules (Image Generation)
AI creates step-by-step annotated visuals for breast self-exam, menstrual cycle, pregnancy stages, bone health, and skin checks.
Enhances health literacy and self-awareness among women.
Doctor Efficiency Tools (Multimodal Decision Support)
Smart triage: AI sorts critical scans first.
Longitudinal tracking: Gemini compares old and new scans.
Enhances doctor productivity and clinical outcomes.
Telemedicine Integration
Remote patients can upload scans.
AI pre-screens before sending to doctor.
Enhances accessibility for rural/low-resource areas.
In summary:
This app leverages Gemini multimodal capabilities to build a full-stack women’s health assistant that helps doctors detect breast cancer faster and educates women on their health journey through visual, annotated, and personalized experiences.
app link : https://ai.studio/apps/drive/1jczl5FeK5f81lG3ekBkOoEKiEIHe-Zyr
Top comments (0)