MediSight.co

Anytime. Anywhere. MediSight strives to equalize healthcare services around the globe, regardless of one's race, gender, or financial circumstances.

Comment

A readme for medisight flask web application.

When we started this project, our primary focus was on simplicity and accessibility. We stripped away all the features that only contribute to complexity. As a result, our service is very easy to install and use. It also means that it's very easy to contribute new features to this project. If you are interested in contributing, please make a pull request and we will review it as soon as possible.

Inspiration

Medisight was inspired by the growing need for accessible healthcare, particularly in remote and underserved areas. The COVID-19 pandemic highlighted the importance of telehealth and remote diagnostics, but we noticed a gap in the integration of AI for disease diagnosis and the need for more secure, reliable video call technology in healthcare. Our vision was to bridge this gap, providing a tool that not only connects patients with healthcare providers but also enhances the quality of diagnosis and consultation through advanced AI capabilities.

Functionality

Medisight is a multifaceted telehealth app that offers two core functionalities: AI-assisted disease diagnosis and secure video calling. Patients can use Medisight to connect with doctors for remote consultations via high-quality, secure video calls. Our AI component, integrated with OpenAI technology, assists in providing preliminary disease diagnosis by analyzing symptoms and patient data. This feature not only aids doctors in their diagnostic process but also helps in triaging patient cases effectively. Moreover, our integration with Infobip for SMS ensures timely notifications and appointment reminders, enhancing patient engagement.

How we built it

Our team utilized a combination of Flask, HTML, CSS, and JavaScript to develop the web app interface and backend functionalities. Flask served as our backend framework, providing the necessary structure for handling requests and integrating AI and video call features. For the AI component, we integrated OpenAI's models to analyze patient data and provide diagnostic suggestions. The secure video call functionality was implemented using Metered, which leverages WebRTC technology for real-time communications. HTML and CSS were used for crafting a user-friendly interface, while JavaScript helped in adding interactive elements to the app.

Challenges

One of the team's largest challenges was figuring out the best way to split up the work load. We had originally decided to split off into front-end and back-end teams, each working on things separately with the goal of connecting them in the end. However, we soon realized that using that approach resulted in some design flaws as many parts of our codebase relied on the connection between the frontend and backend. We ended up swapping to a more collaborative approach, where we made sure to discuss the connections between frontend and backend in our design to avoid conflicts, and frequently aided each other in our design.

Accomplishments

Our team is particularly proud of developing an app that can potentially revolutionize remote healthcare services. Successfully integrating AI for disease diagnosis while maintaining a high standard of accuracy and privacy is a significant achievement. We also managed to create a secure and reliable video calling experience, crucial for sensitive medical consultations. Overcoming the technical complexities to deliver a seamless, user-friendly app is an accomplishment we cherish.

What we learned

Throughout this project, we learned the importance of interdisciplinary collaboration – combining healthcare knowledge with advanced technology. The technical learning curve was steep; we gained substantial experience in integrating various APIs and technologies like Flask, OpenAI, Infobip, and Metered. This project also taught us about the ethical and privacy considerations in developing healthcare applications, especially when incorporating AI and handling sensitive patient data.

Share this project:

Updates