Month: October 2025

Healthcare AI Assistant for Medical Professionals

Posted on | by Ajitha Gunasekaran | Leave a Comment on Healthcare AI Assistant for Medical Professionals

Healthcare AI Assistant for Medical Professionals

An AI-driven healthcare assistant designed to digitize and streamline medical record management for healthcare providers. Using OCR and system integrations, it converts paper-based patient data into structured, accessible digital records. The solution enhances accuracy, operational efficiency, and interoperability across hospital and national health platforms.

Platform Developed

Overview

This platform uses OCR-powered extraction (Gemini 1.5 Flash) and NLP to digitize paper-based patient records for both Allopathy and Ayurveda. It ensures seamless interoperability by connecting with HMS/EHR systems and national platforms like ABHA, drastically improving data accuracy and efficiency.

Problem

  • Many hospitals and clinics still rely on paper-based patient records, leading to difficulties in accessing, organizing, and sharing critical health information.
  • This outdated system causes delays, errors, and inefficiencies in care, while also hindering integration with national digital health platforms like ABHA

Solution

  • AI-Powered Data Extraction: Converts scanned or uploaded paper records into structured summaries using OCR (Gemini 1.5 Flash) and natural language processing, supporting both Allopathy and Ayurveda formats
  • Seamless System Integration: Connects with Hospital Management Systems (HMS), Electronic Health Records (EHR), and national platforms like ABHA for unified data flow
  • Quality Control & Monitoring: Enables users to review, validate, and correct extracted data with real-time progress tracking and error handling
  • Export-Ready Outputs: Provides clean, standardized digital records that can be exported in required formats for clinical use, audits, or regulatory compliance

Key Business Outcomes

  • Up to 70% Reduction in Manual Effort: Automated OCR and AI-powered data extraction minimize the need for manual data entry and paperwork handling
  • 40% Improvement in Record Accuracy: Standardized digital summaries reduce errors and ensure consistent, high-quality patient documentation
  • 3x Faster Onboarding to Digital Systems: Rapid digitization enables quicker transition to HMS/EHR platforms, accelerating digital maturity in hospitals

Technology

Personalized AI Sports Coaching Assistant

Posted on | by Ajitha Gunasekaran | Leave a Comment on Personalized AI Sports Coaching Assistant

Personalized AI Sports Coaching Assistant

An AI-powered sports coaching assistant designed to deliver personalized performance insights for players and teams. Using computer vision and machine learning, it automatically analyzes gameplay, tracks movements, and classifies sports types. The system helps coaches and athletes make data-driven decisions for faster improvement and smarter training.

Platform Developed

Overview

This platform uses YOLO, EasyOCR, and InceptionNet to automate sports analysis, extracting performance metrics, tracking players, and classifying game types across basketball, football, and tennis. It eliminates manual video review, providing scalable, real-time sports intelligence.

Problem

  • Sports organizations and analysts rely on manual video review, leading to a process that is time-consuming and inconsistent for extracting critical performance insights
  • There is a growing need for AI tools that can automatically detect players, track movements, recognize jersey numbers, and classify game types across multiple sports

Solution

  • Multi-Sport Detection & OCR Integration: Used YOLO models and EasyOCR to detect players and accurately recognize jersey numbers across basketball and football videos
  • Team Classification & Tracking: Applied color-based KMeans clustering and YOLO + ByteTrack to classify teams and consistently track player movements in ice hockey and football
  • Tennis-Specific Analysis: Deployed YOLOv12n for tennis player and ball tracking, with additional logic to calculate speed metrics from positional data
  • Sport Classification via Deep Learning: Trained an InceptionNet model to automatically classify the sports type (e.g., Soccer, Basketball, Tennis) from raw video inputs

Key Business Outcomes

  • Comprehensive CV Model Performance: Achieved accurate real-time detection, jersey OCR, and object tracking across diverse sports scenarios
  • Proven Model Versatility: Validated YOLO’s adaptability for multi-sport analytics and integrated deep learning classification
  • Foundation for Scalable Sports Intelligence: Demonstrated scalable AI capabilities for building future real-time, analytics-driven sports platforms

Technology