Inspiration
Our inspiration came from the daily struggles patients face when trying to understand complex medical information in prescriptions and reports. Misunderstandings can lead to impropermedication use and adverse health effects. We aimed to bridge this gap by creating a solution that makes medical information accessible and comprehensible for everyone.
What it does
Our project, IntelliCare, consists of two main components: the Prescription Scanner and the Report Scanner. The Prescription Scanner converts printed prescriptions into digital text, identifies and extracts medicine names, and provides detailed, easy-to-understand descriptions, including usage, dosage, side effects, and more. The Report Scanner digitizes medical reports, processes and summarizes the information, and delivers clear, insightful details about diagnoses, symptoms, and treatments in a patient-friendly format.
How we built it
We built IntelliCare using several advanced technologies. We employed OCR for text extraction, NER models for identifying medical terms, and fine-tuned our custom LLM on Mixtral-8x7B Instruct v1.0 for generating detailed medicine descriptions. For the Report Scanner, we used Sentence Transformers to embed text chunks and stored them in a vector database like Pinecone on AWS Cloud. Retrieval-Augmented Generation (RAG) was utilized to ensure accurate and relevant information retrieval.
Challenges we ran into
One of the main challenges was ensuring the accuracy of OCR technology in extracting text from handwritten prescriptions. Training the NER model to recognize a wide variety of medical terminologies was also a complex task. Additionally, fine-tuning the LLM to provide precise and relevant information required extensive data preprocessing and model optimization.
Accomplishments that we're proud of
We are proud to have created a solution that can significantly enhance patient understanding and safety by making medical information more accessible. Successfully integrating various advanced technologies to provide accurate and user-friendly information is a significant achievement. Our project not only simplifies complex medical jargon but also empowers patients to make informed decisions about their health.
What we learned
Throughout this project, we learned the importance of interdisciplinary collaboration in solving real-world problems. Combining expertise in machine learning, natural language processing, and healthcare allowed us to create a robust and effective solution. We also gained valuable insights into the challenges of medical data processing and the potential of AI in transforming healthcare.
What's next for IntelliCare
The next step for IntelliCare is to integrate online medicine purchasing with streamlined handling of both digital and handwritten prescriptions, making the process even more convenient for users. We also plan to extend our services to hospitals, offering features like seamless prescription processing, inventory management, and patient medication tracking. Our goal is to further enhance healthcare accessibility and efficiency, ensuring better outcomes for patients and healthcare providers alike.
Built With
gemini-pro
generative-ai
keras
langchain
llm
lora
mitral-8x&b
pinecone-api
python
rag
streamlit
tensorflow
transformers
