DEV Community: Saurabh

EVE AI : AI FOR HER HEALTH

Saurabh — Mon, 29 Sep 2025 06:52:55 +0000

This is a submission for the KendoReact Free Components Challenge.

What I Built

EVE / NARI AI is a clinical assistant for women’s health that helps clinicians analyze breast images, prioritize triage, and generate reports, while giving patients clear, patient‑friendly explanations. It includes a RAG-based Breast Cancer Knowledge Box so users can ask grounded questions with context. The app supports role-based workflows (Clinician/Patient), image analysis with BI-RADS assessment, and a dashboard for case review.

KEY FEATURES

AI-Powered Clinical Analysis (for Medical Professionals)

Breast Scan Analysis: Utilizes AI to highlight suspicious regions, provide BI-RADS scores, generate clinical summaries, and assess risk levels.
Cephalometric Analysis: Accurately identifies precise anatomical landmarks on X-rays, crucial for orthodontic imaging and planning. Skin Photo Analysis: Identifies lesions, assesses their characteristics (asymmetry, border, color, diameter, evolution), and provides malignancy risk scores.
Differential Diagnosis Generation: Suggests the most likely differential diagnoses based on provided clinical summaries.
Longitudinal Summary Generation: Compares current and prior medical scans to summarize changes in findings over time.
Clinical Note Drafting: Automates the generation of structured clinical notes (e.g., SOAP format) based on AI analysis.
Second Opinion Generation: Offers AI-powered second opinions on medical scans and initial AI analyses for enhanced diagnostic confidence.
Clinical and Patient Report Generation: Creates detailed clinical reports for healthcare providers and simplified, patient-friendly reports for better patient understanding.

Patient-Facing AI Tools (for Women Patients )

Menstrual & Reproductive Health Tracking: Enables tracking of menstrual cycles, prediction of ovulation, and AI-powered insights into symptoms.
Pregnancy & Postpartum Tracking: Monitors fetal development, common maternal symptoms, and provides tailored health recommendations.
AI Symptom Checker: Offers preliminary AI analysis and suggests next steps based on reported symptoms.
Personalized Health Education: Provides clear, illustrated answers to health questions, making complex medical information accessible.
Medication Helper: Explains prescription information in simple, easy-to-understand terms.
Scan Annotator: Identifies and labels anatomical structures on medical scans for educational purposes, helping patients understand their own imaging.
AI Health Plan Coach: Assists users in creating and tracking personalized wellness plans to achieve their health goals.
Journal Entry Analysis: Summarizes journal entries, identifies overall sentiment, and offers gentle, encouraging suggestions for reflection.

RAG Knowledge Box (Progress Nuclia Integration)

Interactive Chat Interface: Provides a chat-based interface for users to ask breast cancer-related questions.
Contextual Answers: Powered by Progress Nuclia, it offers Retrieval-Augmented Generation (RAG) to deliver grounded answers with relevant citations and context.
Direct Widget Integration: Seamlessly integrated using the official Nuclia widget for robust and reliable knowledge retrieval. Multi-Modal AI Tools
Image Editing with Text: Allows for editing and annotating images based on natural language text prompts.
Educational Image Generation: Generates minimalist, medically accurate illustrations for various educational content needs.

TECH STACK

EVE AI is built with a modern and robust technology stack:

Frontend: React, TypeScript, Vite
UI Framework: Material UI (MUI) for a consistent and responsive user interface.
AI Integration: Google Gemini API (@google/genai) for advanced AI capabilities across various features.
Database: Dexie.js, a powerful IndexedDB wrapper, for efficient client-side data storage.
UI Components: Leverages a comprehensive suite of KendoReact components from Progress for rich and interactive UI elements, including:

PanelBar, PanelBarItem: For collapsible and organized content sections.
Scheduler: For appointment management and display.
DatePicker: For intuitive date input.
Notification: For displaying timely alerts and messages.
Chart, ChartSeries, ChartSeriesItem, ChartCategoryAxis, ChartCategoryAxisItem, ChartValueAxis, ChartValueAxisItem: For visualizing health analytics data.
Button: For interactive actions.
Input: For text input fields.
DropDownList: For selection of options from a list.
Upload: For handling file uploads.
TreeView: For hierarchical navigation in the sidebar.
Splitter, SplitterPane: For flexible layout management, especially in analysis views.
ProgressBar: For indicating loading and processing status.
TabStrip, TabStripTab: For organizing content into tabbed interfaces.
Tooltip: For providing additional information on hover.
ListView: For displaying lists of items with custom rendering.

RAG (Retrieval-Augmented Generation): Progress Nuclia, integrated via its official widget, for intelligent knowledge retrieval.

Demo

demo link : https://eve-ai-kappa.vercel.app/

github link : https://github.com/saurkan/eve-ai

KendoReact Components Used

Buttons, Input
DropDownList
Grid
Upload
ProgressBar
TreeView
Scheduler
Charts
Notification
Tooltip
Splitter
TabStrip
PanelBar
Menu
DatePicker
ListView

AI Coding Assistant Usage

Used an AI assistant to scaffold the UI, wire KendoReact components, and implement role-based views.
Automated integration of license activation, theme import, and component swaps from MUI to Kendo to reach 20+ components.
Assisted in adding a user-facing Gemini API key dialog and updating copy on the landing page.

Nuclia Integration

Integrated a Breast Cancer Knowledge Box that calls a Nuclia Progress knowledge base endpoint for RAG answers with grounded context.
The RAG chat works independently of the Gemini key (Gemini is only needed for image analysis/report generation).

NARI AI : AI for Her Health

Saurabh — Mon, 15 Sep 2025 06:59:50 +0000

NARI AI, is a comprehensive ai powered health platform built for women's health. It operates in two distinct modes:

Clinician Mode: This mode serves as an AI-powered clinical decision support system for medical professionals. It helps doctors and radiologists manage their workflow by automatically triaging patient cases, providing detailed analysis of medical scans, generating draft reports, and offering advanced tools for research and specialized analysis.
Patient Mode: This mode acts as a personal health companion for patients. It aims to empower patients by providing them with simplified, understandable versions of their medical reports, educational resources, and a suite of AI-powered tools to track their health, understand their conditions, and manage their wellness goals.

What problem does this solve?

NARI AI addresses key challenges on both sides of the healthcare experience:

For Clinicians: It tackles issues of workload, diagnostic efficiency, and documentation burnout. By automating the initial analysis of scans and prioritizing cases by urgency, it allows clinicians to focus their expertise where it's needed most. The AI acts as an assistant, drafting reports and providing quick access to research, thereby streamlining the entire diagnostic and administrative workflow.
For Patients: It solves the problem of health literacy and patient disempowerment. Medical terminology and reports are often intimidating and difficult to understand. NARI AI bridges this gap by translating complex information into simple language, allowing patients to ask questions about their results, and providing them with tools to be more proactive and informed about their health journey.

Tech Stack

The application is a modern, single-page web application built with the following technologies:

Frontend Framework: React with TypeScript
UI Component Library: Material-UI (MUI)
Local Database: Dexie.js, which is a powerful wrapper for the browser's IndexedDB. This is used to store patient cases and chat history, enabling offline access and persistence.
AI Integration: The official Google GenAI JavaScript SDK (@google/genai) to communicate with Gemini models.
Client-side Encryption: CryptoJS is used to encrypt data before it's stored in the local database, enhancing privacy.

How does it use the Gemini API?

The application deeply integrates the Gemini API across its features, leveraging its advanced capabilities in multiple ways, as seen in the geminiService.ts file:

Structured JSON Output: It extensively uses Gemini's ability to generate structured JSON output based on a defined schema (responseSchema). This is the backbone of its analytical features, allowing it to turn a medical scan into a detailed object containing risk scores, findings, bounding boxes, recommendations, and more.
Multimodal Analysis (Image + Text Input): For nearly all scan analyses (breast, skin, cephalometric), it sends both the image data and a detailed text prompt to the Gemini model. The model's vision capabilities are used to analyze the image content based on the instructions in the prompt.
Image Generation (imagen-4.0-generate-001): The app uses Gemini to generate new images from text prompts. This is used to create educational illustrations for patients, such as breast self-exam guides, diagrams of fetal development, or visuals for health articles.
Image Editing (gemini-2.5-flash-image-preview): It uses a vision model to perform in-place edits on existing images based on text commands. A user can type "circle the suspicious mass" or "highlight the ribs," and the model returns a new, modified image.
Conversational AI (Chat): It uses the chat API (ai.chats.create) to create specialized AI assistants. These chatbots are given specific system instructions to act as a "patient report explainer" or a "health coach," ensuring their responses are safe, focused, and helpful within their designated role.
Text Generation and Summarization: Standard text generation is used for drafting clinical reports, summarizing research articles, providing second opinions, and creating patient-friendly explanations.

How does it use multimodality?

Multimodality is a core aspect of NARI AI, enabling it to process and generate information across different formats:

Image-to-JSON: The primary diagnostic flow is multimodal. It takes an image (e.g., a mammogram) as input and outputs structured JSON text data that represents the clinical analysis.
Text-to-Image: The educational features are a reverse example. They take a text prompt (e.g., "a diagram of the pelvic floor") and output a new image.
Image & Text-to-Image & Text (Interactive Editing): This is the most advanced use. The app sends an image and a text command (e.g., "color the clavicle blue") to the model. The model processes both inputs and returns a new, edited image along with a text response confirming the action.
Image-in-Chat: The AI Health Plan coach can generate and display images directly within the chat interface in response to a user's text request, making the conversation more visual and engaging.

All Features and Functionalities?

The application is rich with features, divided between the two user modes.

Clinician Features:

Triage Dashboard: View a queue of patient cases automatically sorted by AI-assessed priority (High, Medium, Low).
AI Scan Viewer: An interactive viewer for medical scans with AI findings (masses, lesions) overlaid as color-coded bounding boxes.
Interactive Analysis Chat: A chat interface layered on the Scan Viewer that allows the clinician to give text commands to the AI to edit the image in real-time (e.g., "point an arrow to the calcification").
Advanced Clinical Tools:
- Suggest Differential Diagnoses: Get an AI-generated list of possible diagnoses based on scan findings.
- Draft Clinical Note: Automatically generate a structured clinical note from the analysis.
- Get AI Second Opinion: Request a second AI analysis for confirmation.
- Compare with Prior Scans: An AI-powered tool to summarize changes between a patient's current and previous scans.
Automated Reporting: Generate a detailed clinical report and a simplified patient-friendly report with one click.
Specialized Analysis Modules:
- Cephalometric Analysis: For orthodontists, this tool auto-detects landmarks on an X-ray and provides instant geometric and angular measurements.
- Breast Cancer Analysis: A dedicated view for breast imaging with specialized tools.
AI Research Assistant: A chat interface for asking complex clinical questions and receiving summarized, evidence-based answers.
Education Studio: A tool to generate custom medical illustrations from text prompts for patient education.

Patient Features:

Health Hub Dashboard: A central "bento grid" dashboard giving access to all personal health modules.
My Reports & AI Explainer: View simplified, easy-to-understand versions of your medical reports. Includes an integrated AI chatbot to answer questions about the report in plain language (e.g., "What does BI-RADS 2 mean?").
AI Symptom Checker: Describe symptoms to receive a preliminary analysis of possible conditions and suggested next steps.
Personalized Health Education: An educational portal where you can ask any health question and receive a clear explanation, often with a custom-generated visual aid.
AI Medication Helper: Upload a photo or type the text of a prescription to get a simple explanation of its purpose, dosage, and side effects.
Interactive Scan Annotator: An educational tool to upload a medical scan (like an X-ray) and have an AI label and explain the visible anatomical structures.
Menstrual & Reproductive Health: Track your cycle and symptoms to receive AI-powered insights.
Pregnancy Tracker: Enter your due date to get weekly updates on fetal development, complete with AI-generated illustrations.
AI Health Plan: A conversational tool to chat with an AI health coach, set a wellness goal (e.g., "improve sleep"), and co-create a simple, actionable plan.

app link : https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%5B%221jczl5FeK5f81lG3ekBkOoEKiEIHe-Zyr%22%5D,%22action%22:%22open%22,%22userId%22:%22104493919399313468318%22,%22resourceKeys%22:%7B%7D%7D&usp=sharing

demo video link :

This is a submission for the Google AI Studio Multimodal Challenge

Curio-Shorts: An AI Shorts App That Cures Brain Rot

Saurabh — Mon, 15 Sep 2025 03:39:01 +0000

CurioShorts is an AI-powered educational content generator. It transforms any user-asked question into a short, engaging, TikTok-style shorts video. Users can select a fun character (like Spider-Man or a custom one) to be the narrator, choose a visual art style, and the app automatically generates a simple song with lyrics, custom images for each line, and a voice-over narration, all explaining the answer to the question with music in background.

What Problem It Solves
The app directly tackles the problem of "brain rot"— the passive, often low-value content consumption common on short-form video platforms. Its goal is to "swap brain rot for brain fuel" by making learning as engaging, accessible, and fun as scrolling through social media. It provides a creative and educational alternative, especially for younger audiences.
This app could be gamechanger for children to cure their brain rot. Tiktok, YouTube Shorts, Insta Reels can use this to make their educational mode of their respective apps. Replacing mindless scrolling with scrolling which will make them learn new things in very interactive way.

Tech Stack

Frontend: TypeScript, HTML5, CSS3
AI Model: Google Gemini API (@google/genai)
Client-Side Storage: IndexedDB for persisting generated shorts.
Browser APIs:
Web Speech API (SpeechSynthesis) : For text-to-speech voice narration 2. Intersection Observer : To manage video playback efficiently as the user scrolls
Libraries: marked for rendering lyrics.

Demo
app : https://ai.studio/apps/drive/1o-mWyYV79577vjypSg8wIzIjMHyVfWu0

youtube demo link :
screenshots :

How I Used Google AI Studio
This app completely built in Google AI Studio. The core logic, including the prompts for generating song structures and image descriptions, was developed and tested in AI Studio's flexible environment before being integrated into the application code with the Gemini API.

Multimodal Features
The app taps into Gemini’s multimodal capabilities through a two-step pipeline:

Structured Text Generation (gemini-2.5-flash): The user’s question is passed to the model with instructions to act as a songwriter. The model outputs a JSON structure containing the song’s lyrics, a detailed image prompt for each lyric line, and a suggested music style.
Text-to-Image Generation (gemini-2.5-flash-image-preview): Each image prompt is then fed into Gemini’s image model, producing unique, stylized visuals that match the lyrics. By chaining these steps, the app turns a single text query into an integrated audio-visual learning experience, blending storytelling, imagery, and music.