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Login Page
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patient regular data and symptoms to disease prediction
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Suggest Medicine according to disease
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Suggest Ayurvedic remedy to cure disease
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AI powered Text Summarizer. Can summarize any Medical Report
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Patient data
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Bill generator of patient according to disease medicine
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Authority can add new disease, symptoms, medicines and medicines prices
Inspiration
Inspiration for this project came from observing how many small clinics still rely on manual record keeping and handwritten prescriptions. This can make it difficult to track patient history and manage information quickly. I wanted to create a simple digital tool that could help doctors diagnose illnesses based on symptoms, maintain patient records, and automate billing in one system.
What it does
AI Hospital Assistant helps doctors manage basic clinical tasks efficiently. It diagnoses possible diseases based on patient symptoms, suggests medicines, stores patient records, generates bills, and keeps a complete patient history. It also includes an AI-powered text summarizer for medical documents and a section for simple Ayurvedic remedies for common health issues.
How we built it
The project was built using Python with CustomTkinter for the graphical user interface. Patient and disease data are stored using SQLite. Disease prediction is done by matching patient symptoms with stored symptoms using similarity matching. For AI features, the text summarization is powered by Hugging Face Transformers.
Challenges we ran into
One challenge was designing a reliable way to match symptoms with diseases using text similarity. Another challenge was integrating AI-based text summarization and extracting readable text from PDF files. Managing multiple features like diagnosis, patient history, billing, and remedies in a single interface also required careful design.
Accomplishments that we're proud of
We successfully built a complete desktop healthcare assistant that combines disease diagnosis, medicine suggestions, patient record management, billing, and AI-powered text summarization in one application. Creating a clean and functional interface while integrating multiple technologies was a major achievement.
What we learned
Through this project, we learned about GUI development, database management, text similarity algorithms, and integrating AI models into real applications. We also improved our problem-solving skills and learned how to organize a large Python project efficiently.
What's next for AI Hospital Assistant
In the future, we plan to add voice input for symptoms, cloud database support, medicine inventory management, and more advanced AI diagnosis models. We also aim to convert the application into a web or mobile platform so it can be used by clinics anywhere.
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