MediExplain

Inspiration

This challenge really came to the forefront after one of our group members recalled how their mother had to go to the hospital due to improperly taking their blood thinner medication, due to a misinterpreted prescription caused by a language barrier and medical illiteracy. With further research into the millions of people who misuse their medication, this experience made us realize just how important access to medical treatment is and how essential proper communication is for it to be properly used.

What it does

Our project allows the user to take medical documents or information—such as doctor notes, diagnoses, prescriptions, or lab results—and translate them into clear, easy-to-understand explanations. It also translates these documents into the user's language of choice, with information being available in text and audio form.

How we built it

We built MediExplain by collaborating through a shared repository on GitHub, which allowed our team to manage the project code and coordinate development during the hackathon. First, we cloned the repository locally so each team member could work on different components of the application simultaneously. The front-end was developed to provide a user interface where users can input medical text or information, while the back-end handled processing and communication with the AI model that generates simplified explanations of medical terminology. We used GitHub for version control, enabling us to push updates, track changes, and resolve merge conflicts as the project evolved. Throughout development, we integrated the front-end and back-end components so that user input could be sent to the AI processing system and the generated explanation could be returned to the interface. This collaborative workflow allowed our team to rapidly prototype and build a working medical education tool within the limited timeframe of the hackathon.

Challenges we ran into

During the hackathon, our team encountered several challenges while developing our project. One of the main issues was configuring the front-end and back-end to work together properly, as integration between the two environments required troubleshooting and adjustments. We also experienced bugs and version control issues in our GitHub backend, which occasionally slowed down development and required additional time to resolve merge conflicts and fix errors. Additionally, because our team members were collaborating remotely from different locations, we faced communication challenges that made coordination and real-time problem-solving more difficult. Despite these obstacles, we worked together to debug the issues and continue progressing on the project.

Accomplishments that we're proud of

One of our main accomplishments during the development of MediExplain was successfully creating a working prototype within the limited time of a hackathon. We were able to design and implement a full-stack application that connects a user-friendly front-end interface with a back-end system capable of processing medical text and generating simplified explanations. Another achievement we are proud of is integrating AI to translate complex medical terminology into language that is easier for patients to understand, helping address the issue of low health literacy. Additionally, despite facing technical challenges such as front-end and back-end configuration issues, GitHub bugs, and remote communication barriers, our team was able to collaborate effectively and deliver a functional product. Overall, we are proud that we were able to turn an idea into a practical tool that has the potential to make medical information more accessible and understandable for patients.

What we learned

During this GitHub project, we learned several important lessons about both technical development and teamwork. One of the main things we learned was how to properly structure and integrate a full-stack application, connecting the front-end user interface with the back-end logic that processes user input and generates responses. We also gained experience using GitHub for version control, including managing branches, pushing updates, resolving merge conflicts, and collaborating on shared code efficiently. In addition, we improved our debugging skills by identifying and fixing issues that occurred when integrating different parts of the system. Finally, the project helped us strengthen our communication and coordination skills while working remotely, teaching us how to divide tasks, share progress, and solve problems together within a limited hackathon timeframe.

What's next for MediExplain

The next step for MediExplain is improving the scalability of the platform and expanding its features to provide more comprehensive support for patients. To scale the application, we plan to improve the backend infrastructure so it can handle a larger number of users and process medical information efficiently through cloud-based deployment and optimized APIs. In terms of future extensions, MediExplain could include AI-generated summaries of expected symptoms and potential medication side effects, as well as guidance on recommended behaviours and possible conflicts with lifestyle choices, other medications, or certain foods. Users could also input ongoing symptoms, side effects, or lack of improvement and receive AI-generated feedback to help them better understand their situation. Additional features may include text-to-voice functionality for accessibility, medication dosage tracking with reminders for required pill intake over a user-defined schedule, and AI-powered recommendations suggesting when it may be appropriate to visit a doctor. These improvements would help MediExplain evolve from a simple explanation tool into a more interactive and supportive health management assistant.