Laiba Khan@laibakhan

“MedicQuick AI” is a Streamlit framework website application that works as a medical assistant allowing users to track symptoms and get analysis using an artificial intelligence (AI) model, to upload medical images for lung disease analysis, generate PDF reports based on symptoms and analysis, to visualize symptom and pain trends and to chat with a medical assistant powered by the Falcon 180B Model. A symptom tracker is established so users get described symptoms and receive analysis from an AI model (Falcon 180B). It generates a downloadable PDF report with the analysis and recommendations. Medical image analysis is addressed to upload X-rays or similar images for simulated analysis of lung diseases (cancer, pneumonia, COVID-19). In addition, users can visualize trends in their symptoms and pain levels over time, along with a pie chart showing symptom distribution. Finally, a medical chatbot lets the users chat with a medical assistant powered by another AI model, asking questions and receiving responses. This is a prototype, and AI analysis should not replace professional medical advice. The team members participated in different roles such as front and back-end development, AI engineering, business deployment, and project management, making the idea into a simple app. Modern healthcare challenges include diagnostic delays, error rates, resource constraints, and patient communication. As a result, MedicQuick AI leverages the Falcon 180B Model to enhance healthcare with advanced symptom analysis, report generation, and real-time patient support. Why MediQuick AI? Advanced AI Models analyze patient symptoms to suggest potential diagnosis. Furthermore, AI-powered X-ray image scanning lets the users determine whether they have COVID-19, Pneumonia, or Lung Cancer. Important prototype key features include the Falcon 180B AI model for accurate symptom analysis and PDF reports generation for diagnostic data comprehension.

## Introduction In the software industry, creating software is a passion, but dealing with bugs is a headache. Similarly, in the data industry, working with clean data is a joy, while cleaning it is a chore. This year, many voiced concerns about AI taking over their jobs. They want AI to automate tasks they dislike, freeing up time for what they love. As one person put it, "I don't want AI to automate creative designing so that I can do my laundry, I want it to do my laundry so that I can design." This sentiment inspired me to create **Clean It**, a data cleaning service to tackle industry problems at their core. ## How It Works? **Clean It** is a multi-agent system that streamlines the data cleaning process through a sequential pipeline. Each agent has a specific set of tools and tasks, working in a linear fashion. This ensures that prerequisites are met to prevent data loss. All activities are logged for transparency. Simply input your data, click "clean it," and the logs will be available for review until you refresh the application, eliminating the need for constant monitoring. ## Key Features - **Automated Data Cleaning**: Clean It handles missing value imputation, outlier detection and removal, data standardization, and error correction. - **Accuracy**: Powered by the cutting-edge o1-mini model, ensuring top-notch performance. - **User-Friendly Interface**: Features a simple and intuitive interface for all users. ## How Clean It is built Clean It leverages the latest technology, utilizing Streamlit, a popular Python library, for a user-friendly interface. Its AI model, powered by the o1-mini large language model by Meta, identifies and solves complex data issues. ## Conclusion With **Clean It**, the data industry gains a powerful tool to automate data cleaning, allowing professionals to spend more time extracting valuable insights. This automation empowers informed decisions and drives data innovation.

The project is designed to streamline the user form-filling process by leveraging database integration and automated validation. Users enter a unique ID, which triggers the system to fetch their relevant data from the database. The system identifies and highlights missing fields, incorrect formats, or any inconsistencies in the entered data. Upon submission, it automatically corrects errors by fetching accurate information from the database, ensuring data integrity and completeness. This process minimizes manual input, reduces errors, and improves efficiency. The project simplifies form handling, delivering a user-friendly and reliable solution for data validation and accuracy during form submission.