Cancer Symptoms Checkup Software Development

Project Information

  • Client:Checkup Cancer
  • Date:Jun 01, 2023
  • Author: Dr krishna Anand
  • Place: Delhi, India

Cancer Symptoms Checkup Software Development

We develop AI-powered Cancer Symptoms Checkup software that helps users assess potential symptoms early and guides them toward appropriate medical action.

Client Overview

A forward-thinking medical team aimed to streamline early cancer symptom detection and help users identify potential risk factors through a user-friendly digital platform. They sought a robust, scalable, and intelligent software solution that could serve as a reliable first-touch diagnostic tool.

The Challenge

The client needed a responsive and secure web-based system that could analyze a wide range of user-submitted symptoms and provide insights based on validated medical logic. The platform had to ensure data privacy, offer intuitive UX, and be flexible for integration with future AI modules or telemedicine tools.

Our Approach

Shrinext HealthTech developed a comprehensive Cancer Symptoms Checkup platform using Laravel (PHP) for the backend and Vue.js for a dynamic, reactive frontend. The solution enables users to enter symptoms, receive preliminary assessments, and get personalized advice or referral suggestions.

Our development included:

  • A medical knowledge graph-backed logic engine
  • Dynamic questionnaire flow tailored to symptoms
  • GDPR-compliant data protection and encryption protocols
  • Dashboard for medical team oversight and updates
  • Scalable architecture for future AI/ML integration

Technology Stack

  • Backend: Laravel (PHP Framework)
  • Frontend: Vue.js
  • Database: MySQL
  • Security: End-to-End Encryption, Role-Based Access
  • Compliance: HIPAA-Ready, GDPR-Conformant

Outcome

The platform successfully empowered patients to perform guided self-assessments from the comfort of their homes, increasing awareness and promoting early detection. Medical professionals gained a reliable digital tool to extend outreach and reduce unnecessary clinic visits while maintaining clinical accuracy.

The solution is now being considered for broader deployment across oncology support networks and primary care units.

Impact

  • Reduced diagnostic delays through early symptom checks
  • Strengthened doctor-patient communication pipeline
  • Enhanced data tracking and analytics for public health planning
  • Scalable foundation for AI-powered health applications
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