Diagnosis Guard

Inspiration

DiagnosisGuard was inspired by the critical need for improved patient safety and diagnostic accuracy in healthcare. We were motivated by cases where patients went undiagnosed after seeing multiple doctors, and how AI tools like ChatGPT have helped identify overlooked conditions.

What it does

DiagnosisGuard is a web app that enhances patient care by:

Collecting detailed patient information, including symptoms, diagnoses, and test results
Analyzing this data using advanced AI and comparing it with up-to-date medical knowledge
Identifying potential oversights or missed diagnoses
Suggesting additional tests or examinations
Providing patients with informed questions to ask their healthcare providers

How we built it

We developed DiagnosisGuard using:

A user-friendly web interface for data input
Integration with the Perplexity AI API for advanced natural language processing
Comprehensive medical databases for accurate comparisons
Secure data handling to ensure patient privacy

Challenges we ran into

Balancing the need for detailed information with user-friendly input methods
Ensuring the AI's suggestions complement rather than replace professional medical advice
Handling the complexity and variability of medical data and diagnoses

Accomplishments that we're proud of

Creating a tool that potentially saves lives by catching critical details
Developing an AI system that can interpret and analyze complex medical information
Empowering patients to take an active role in their healthcare decisions
Strong improvement over ChatGPT due to the use of reliable web sources via Perplexity

What we learned

The importance of AI in augmenting healthcare decisions
The complexity of medical diagnosis and the value of cross-referencing information
The critical balance between technology and human expertise in healthcare

What's next for Diagnosis Guard

Expanding our medical knowledge base for more comprehensive analysis
Developing partnerships with healthcare providers for real-world testing and implementation
Exploring integration with electronic health records for more accurate and efficient analysis
Implementing machine learning to improve diagnostic suggestions over time