Medical Helper

Inspiration

Patients often face challenges when trying to understand their treatment plans. Medical terminologies, with their complex language and technical nuances, can be particularly hard to grasp for those without a science background. This gap in understanding not only affects patient confidence but can also influence treatment decisions and financial planning.

What it does

The Medical Helper project simplifies complex medical terms, making them more accessible to patients. It allows users to upload medical documents or manually enter medical diagnoses or treatments. The system then extracts and summarizes medical text, finds relevant research articles, and explains medical terms using AI.

How we built it

We used pypdf to extract text from PDF documents, and Hugging Face's transformers to summarize medical content. We then used NCBI Entrez API to search relevant research from PubMed. OpenAI's GPT-4 is used to explain medical terms in an easy-to-understand manner.

Challenges we ran into

Since we are all beginners and our first-time hackathon, we didn't know how to start first. Also, the technical challenges are one thing that we faced the most.

Accomplishments that we're proud of

We are proud of attending Biohacks as our first hackathon experience, and we are excited to generate ideas that can change our healthcare system to be more inclusive and accessible.

What we learned

We learned a lot of new things by joining the workshops and collaborating together to generate ideas that can make our society better with technology and AI.

What's next for Medical Helper

The next for Medical Helper will be integrating multilingual translation support to enhance inclusivity for patients with different ethnic backgrounds. Also, we want to have customization features according to patients' needs. Eventually, we hope to develop a web-based or mobile application that allows voice input and output to make the tool easily accessible to a wider audience.