Inspiration
Medical professionals struggle with long reports, unstructured data, and unclear research articles. We wanted to build an AI tool that enhances efficiency in the medical field.
What it does
It summarizes medical texts, refines research articles, and sanitizes medical data for accuracy.
How we built it
We used Google Gemini for NLP, Python for backend processing, and structured prompts for AI refinement.
Challenges we ran into
Handling complex medical terminology and ensuring accurate AI-generated content.
Accomplishments that we're proud of
Successfully automating text summarization, refinement, and data sanitization for medical applications.
What we learned
The importance of clear prompts and fine-tuning AI models for domain-specific accuracy.
What's next for AI-Powered Medical Text Processing Agent
Adding NER-based entity extraction, speech-to-text transcription, and bias detection for medical reports.
Built With
- python
- streamlit
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