• Automate Image Analysis: Develop AI models that can automatically detect and classify abnormalities in medical images (e.g., X-rays, CT/MRI scans) related to TB, COPD, lung cancer, and silicosis.
• Prioritize Critical Cases: Implement triage systems to prioritize high-risk cases, flagging them for immediate review by healthcare professionals.
• Integrate with Existing Systems: Ensure seamless integration of AI solutions with existing clinical workflows and hospital IT systems (e.g., PACS), including user-friendly interfaces for non-technical staff.
• Streamline Diagnostic Processes: Shorten the time required for diagnoses and support more consistent outcomes using AI-enabled tools.
• Ensure Scalability and Accessibility: Make AI solutions accessible in both urban and remote areas, ensuring compatibility with limited infrastructure such as internet connectivity or hardware in rural settings.

• Faster Diagnostic Turnaround: Reduced wait times for imaging results, allowing for timely interventions and treatments.
• Improved Diagnostic Accuracy: AI-driven insights to support more accurate and consistent identification of TB, COPD, lung cancer, and silicosis in medical images.
• Reduced Radiologist Workload: Automation of routine image analysis tasks, enabling radiologists to focus on high-priority cases and complex diagnoses.
• Increased Healthcare Access: Enable healthcare facilities in remote or resource-constrained areas to conduct AI-powered diagnostics even with limited infrastructure.
• Compliance with health data protection laws.
• Scalable Integration: A sustainable solution that can be expanded across multiple healthcare facilities with seamless integration into existing systems.

The goal of this challenge is to implement an AI-based system that:

• Predicts high-risk pregnancies using maternal health data.
• Provides real-time decision support for timely referrals to CHCs, district hospitals, or tertiary centers.
• Improves maternal and fetal outcomes by enabling earlier detection and intervention.

• Improved Risk Prediction: AI model delivers accurate high-risk scores, minimizing missed cases.
• Optimized Referrals: Automated referral guidance ensures patients are sent to the right facility at the right time.
• Efficient Healthcare Resource Use: Reduces unnecessary referrals and overload on tertiary centers.
• Better Maternal and Fetal Health: Early identification of risks leads to better care and outcomes.
• Enhanced Accessibility: Lightweight, offline-capable mobile app for rural healthcare workers to easily input data and receive AI support.
• Adhere to Data Privacy Regulations: Comply with health data protection laws.

• Simplify Scheme Discovery: Provide an AI-powered conversational interface that helps MSME owners identify relevant schemes quickly and accurately.
• Personalized Scheme Recommendations: Tailor scheme suggestions based on the specific profile of the MSME, including business type, turnover, and district.
• Enhance Application Process: Assist MSMEs in understanding documentation requirements, deadlines, and workflows with interactive checklists and potential form auto-fill capabilities.
• Increase Accessibility: Ensure the chatbot is accessible across multiple channels (web, mobile) with regional language support for a broader audience.
• Continuous Learning and Improvement: Implement feedback loops and continuous updates to improve the chatbot’s ability to provide accurate and relevant information.

• Reduced Application Complexity: Simplified and personalized guidance on scheme eligibility, application steps, and documentation requirements, leading to more accurate and complete applications.
• Increased Awareness of Available Schemes: MSMEs will be more aware of the financial incentives, technology upgrades, and support schemes available to them, fostering growth.
• Improved MSME Access to Government Support: By automating and personalizing the process, MSMEs will be able to more easily apply for schemes that match their needs, reducing administrative burdens.
• Enhanced User Experience: MSME owners will find it easier to interact with the platform through a simple, conversational chatbot, improving engagement even for those with limited tech expertise.
• Omnichannel Accessibility: MSMEs, especially those in rural areas with limited connectivity, will be able to access the chatbot via web and mobile increasing the reach and usability of the service.
• Data-Driven Insights: The chatbot’s ability to capture feedback and refine its responses will enhance the relevance and accuracy of the information provided over time, improving its effectiveness for MSME owners.
• Scalable and Future-Proof Solution: The system can be enhanced to include features such as direct application submission and real-time status tracking, ensuring long-term scalability and continuous improvement.

• Maximize Revenue: Utilize TGSRTC’s pricing data to guide revenue calculations, ensuring buses are assigned to routes and times where they can earn the highest total revenue.
• Maximize Seat Occupancy: Align bus supply with demand to minimize empty seats and reduce overcrowding, ensuring efficient bus utilization.
• Accurate Demand Handling: Forecast both paid and free (Mahalaxmi) rider demand, ensuring that capacity planning reflects actual occupancy for both paid and free riders.
• Adherence to Operational Constraints: Ensure bus allocations adhere to fleet limits, mandatory service requirements, and seat capacity constraints for each route.

• Optimized Bus Allocation: AI-driven route optimization ensures buses are deployed where demand is highest, improving revenue potential and seat occupancy.
• Increased Revenue: By optimizing routes and times, TGSRTC can capitalize on untapped revenue opportunities and reduce missed revenue due to unallocated or underutilized buses.
• Improved Operational Efficiency: The system ensures bus deployments are aligned with actual demand (including Mahalaxmi riders), reducing overcrowding on high-demand routes and minimizing empty buses on low-demand ones.
• Accurate Forecasting: Enhanced demand forecasting considering historical trends, seasonality, and events helps create a more accurate and responsive bus allocation schedule.
• Enhanced Mahalaxmi Program Integration: Mahalaxmi passengers are factored into the seat allocation process, ensuring capacity planning is accurate and reflects the true demand.
• Scalability and Flexibility: The system is scalable to handle hundreds of routes and thousands of departures, providing a feasible solution that can be updated daily or on-demand based on new data.
• Improved Service Levels: By identifying peak and slack hours and assessing boarding/alighting points, the system helps improve service efficiency and identify opportunities for service improvements.

Develop and deploy an AI-powered Draw-and-Learn tool that:

• Enhances vocabulary development in students through interactive, visual learning.
• Supports multilingual education by providing word recognition in English, and Telugu.
• Offers real-time, personalized feedback to improve student engagement and learning outcomes.
• Ensures the tool is usable in low-connectivity environments and requires minimal hardware, making it accessible across diverse schools in Telangana.
• Gathers continuous feedback from teachers to fine-tune the AI model and ensure long-term improvements.

• Increased Student Engagement: The tool’s interactive, creative approach will increase student interest and participation in vocabulary development.
• Improved Vocabulary Retention: By associating words with images and drawing activities, students are likely to retain new vocabulary better.
• Support for Multilingual Learning: The tool’s multilingual capabilities will ensure all students, regardless of their language background, have access to the same learning opportunities.
• Personalized Learning: AI-driven feedback and adaptive learning will cater to individual student needs, improving learning speeds and comprehension.
• Scalable Solution: The system will be scalable across schools in Telangana, with offline capabilities for remote areas and a low hardware footprint.
• Continuous Improvement: Regular updates to the AI model based on teacher feedback and real-time data will ensure the tool becomes more robust over time, continuously improving vocabulary development for students.

To implement an AI-driven solution that detects discrepancies in real-time by:

• Comparing pre-registration data with documents.
• Validating market values using location-based checks against circle rates.
• Providing immediate alerts to ensure timely correction.

• Fewer Errors in Documents: Customers can be confident and assured that the data entry/ index pertaining to registred documents is accurate resulting in error-free Encumbrance Certificate.
• Faster Registration Process: With real-time checks, the overall time taken to process and approve documents is significantly reduced.
• Fair and Transparent Valuation: Property values are validated against official rates based on location, ensuring fair Valuation and transparency.
• Reduced Hassles and Rework: Immediate alerts help address issues early, minimizing delays, repeated visits, or document corrections.
• Greater Trust in the System: A more reliable and efficient process increases customer trust in the registration and stamping system.