Telangana AI Rising Grand Challenge - Calling all Visionary AI Startups!

Conditions such as Tuberculosis (TB), Chronic Obstructive Pulmonary Disease (COPD), lung cancer, and silicosis rely heavily on timely and accurate X-ray or CT/MRI imaging for diagnosis. The department aims to improve efficiency of radiologists and reduce patient wait times to enhance turnaround time of diagnostics. Additionally, the adoption of AI in medical imaging faces challenges such as integration with existing clinical workflows and ensuring accessibility in remote areas with limited connectivity.

• 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.

In Telangana, the healthcare system operates across three tiers—primary, secondary, and tertiary—with varying levels of care and resources. High-risk pregnancies, when missed, can lead to life-threatening emergencies, while unnecessary referrals can overwhelm higher-tier centers.

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.

Micro, Small, and Medium Enterprises (MSMEs) are crucial for economic growth but often struggle to navigate the complex and large number of government schemes available for support. Many MSMEs are unaware of schemes or face difficulties understanding eligibility criteria, requirements, and application procedures, which results in missed opportunities for financial incentives and growth. With over 80 MSME schemes at central and state levels, the process is often cumbersome and time-consuming, especially for smaller businesses with limited resources. The lack of personalized, step-by-step guidance and difficulties with digital tools further hinder MSMEs from accessing relevant opportunities.

• 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.

The Telangana State Road Transport Corporation (TGSRTC) operates thousands of buses serving millions of passengers daily, but faces significant challenges in optimizing bus allocations across its routes. Certain routes experience underutilized buses at certain times, while others face overcrowding, leading to inefficiencies in bus deployment and missed revenue opportunities. The department wants to improve the operation effectiveness and meet the demand efficiently keeping in mind the Mahalaxmi program which offers free tickets to women passengers.

• 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.

Traditional vocabulary learning methods in Telangana schools rely on memorization, limiting student engagement and interaction. Additionally, there is a pressing need to support multilingual education and offer personalized feedback. This challenge outlines the need for an interactive, accessible solution that promotes vocabulary development in English and Telugu.

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.

The Department of Registration and Stamps processes thousands of property documents daily. The department needs to ensure in a systemic, tech-enabled way that there are no errors / discrepancies in the registration process.

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.