MediCo App
App for doctors to diagnose patient reports faster with AI made with Streamlit
Created on 8th November 2024
•
MediCo App
App for doctors to diagnose patient reports faster with AI made with Streamlit
The problem MediCo App solves
Intelligent Chatbot for Assistance - A chatbot answers doctors questions using web search results, medical texts, and records of the patient, providing reliable information and guidance on medications and next steps for faster and better diagnosis.
AI-Assisted Analysis for Better Diagnoses - Integrated AI analyzes past reports and current symptoms, assisting doctors with insights that support more accurate and quicker diagnoses.
Access to Comprehensive Patient Data -Doctors can securely view detailed patient records, past reports, and ongoing medical history, ensuring they have all necessary information to provide accurate, personalized care.
Streamlined Diagnostic Support with AI Chatbot - The integrated chatbot assists doctors in making faster, data-backed diagnoses by analyzing patient reports and symptoms. It can reference medical databases and resources, saving time on research.
Real-Time Report Analysis - Doctors can instantly analyze new patient reports, with the AI highlighting critical information or abnormalities, allowing doctors to make timely decisions.
Quick Access to Medical Knowledge - The chatbot can pull information from preloaded medical texts or perform web-based lookups, offering up-to-date medical insights to support the doctor’s decision-making process.
Centralized Data Management - All patient data, including diagnostics and reports, are stored and accessed in one place, reducing administrative burdens and ensuring a more organized, efficient workflow.
Enhanced Patient Safety - Doctors have quick access to full patient histories, preventing oversight of key health details and reducing risks of misdiagnosis, improving patient safety and care quality.
Challenges we ran into
A key hurdle we encountered was implementing the AI chatbot to support real-time diagnostic assistance for doctors. The challenge was ensuring that the chatbot could reference relevant medical data such as patient reports, preloaded medical texts, and real-time web searches while maintaining fast response times.
Initially, our chatbot struggled to handle large amounts of patient data and multiple reference sources, leading to delays and incomplete responses. To overcome this, we optimized the chatbot’s data handling by prioritizing critical patient data and using context-specific prompts so that the AI only focused on the most relevant information. We also integrated caching for frequently accessed medical texts, which helped reduce loading times when the chatbot needed to reference those sources.
Tracks Applied (3)
Best Use of MongoDB Atlas
Major League Hacking
Best Use of Streamlit
Major League Hacking
Best use of GitHub
GitHub Education
Technologies used

