MaxCareAI

Inspiration

"MedicareAI" was inspired by the frustration and uncertainty many face when trying to determine the severity of their symptoms and deciding the appropriate course of action. Whether it's a sudden illness, injury, or persistent symptoms, knowing when to seek medical help can be difficult. With "MedicareAI," we aimed to create an AI-powered web app that guides users through the decision-making process based on their symptoms, making healthcare more accessible and less stressful. Our goal is to provide immediate recommendations and help users find nearby medical facilities, ensuring they get the care they need quickly.

What it does

"MedicareAI" is a web app designed to help users assess the severity of their symptoms, providing them with recommendations on whether to consult a doctor, visit a walk-in clinic, or go to the hospital. By analyzing the user’s symptoms using an AI model, the app determines the appropriate next steps. Additionally, it integrates with a map to suggest nearby hospitals, clinics, and medical centers based on the user's location. This platform helps users make informed decisions and seek the right care, promoting better health outcomes.

How we built it

We built "MedicareAI" using React for the frontend to create a smooth and user-friendly interface. For the backend, we used Flask to manage the app’s functionality and integrate the AI model. To develop the core of our application, we used Gumloop to train our GPT-based GenAI model. Gumloop enabled us to work with a robust dataset on patient symptoms and their severity, allowing the model to learn how to analyze user inputs and provide tailored recommendations. The AI model processes symptom descriptions from users and generates severity assessments, guiding them on whether to consult a doctor, visit a clinic, or head to a hospital.

Challenges we ran into

One of the biggest challenges was training and fine-tuning the AI model to accurately assess symptom severity based on user inputs. Ensuring the model’s reliability was crucial, as users would depend on it to make important health decisions. Another obstacle was mixing react with Python/Flask, which can be a stressful to work with at times.

Accomplishments that we're proud of

We’re proud to have developed a tool that not only provides valuable health recommendations but also helps users take proactive steps towards improving their health. The app effectively analyzes symptoms and offers real-time suggestions, giving users peace of mind when they’re uncertain about their health. Additionally, our successful integration of the AI model and Mapbox API helped us build a robust platform. This project has also deepened our understanding of AI model integration and location-based services, skills we’re excited to apply in future projects.

What we learned

"MedicareAI" was a great learning experience for our team. We gained hands-on experience in integrating AI models into a web app, working with healthcare data, and using machine learning for decision-making. We also learned how to implement real-time location services using the Mapbox API and how to design a user-friendly interface with React. This project also taught us how to collaborate efficiently and manage both technical and user-focused requirements.

What's next for MaxCareAI

While we’re proud of the app’s current functionality, there are many improvements we’d like to implement in the future. Some ideas include expanding the AI model to cover more symptoms and conditions, adding personalized recommendations based on medical history, and enabling user authentication for privacy and security. Additionally, we want to add a navigation feature so users can easily find directions to the recommended facilities.

Built With

• flask
• gumloop
• javascript
• kiddle
• python
• react