## Inspiration

Living in an era of rapid technological advancement, we saw the potential for AI to bring significant improvements to healthcare accessibility and efficiency. The idea for HealThrive AI was born out of our desire to provide an easy-to-use health tool that empowers individuals to take control of their health, particularly in regions where health facilities might not be as readily available or for those living with constant health worries.

## What it does

HealThrive AI operates as a user's personal health assistant right in their pocket. By inputting their symptoms, they receive advice and insights about their health condition based on cutting-edge AI technologies. Furthermore, HealThrive AI adjusts its recommendations for medication reminders, diet observations, and much more, offering health management that is both personalized and proactive.

## How we built it

We sought a two-phased approach for developing HealThrive AI. For the backend, we chose Django, which is a powerful and flexible Python framework that allowed us to handle and process large volumes of data safely and efficiently. The frontend was built in React, allowing a seamless, user-friendly experience due to its ability to build UI components efficiently. But the true magic behind the app lies within the integration of OpenAI's GPT-3 API, providing the AI capabilities that drive health advice.

## Challenges we ran into

The biggest challenge we faced was implementing the OpenAI's GPT-3 API and ensuring its precise functioning in providing health advice. Accurate medical data interpretation and response generation is a critical factor, and getting it right was no small feat. We also faced challenges in handling the vast, diverse, health-related inputs for smooth and efficient machine learning.

## Accomplishments that we're proud of

Our greatest accomplishment was creating an app that has potential to revolutionize the way people manage their health. Designing a user-friendly interface with complex AI integration running smoothly in the backend is something we take great pride in. The effective incorporation of GPT-3 and building a model that provides reliable health advice are noteworthy accomplishments.

## What we learned

Apart from intensifying our technical skills, we also learned the importance of intuitive design and user experience in healthcare technology. We learned how to integrate advanced machine learning models effectively and also experienced the challenges and potential solutions in handling diverse medical data.

## What's next for HealThrive AI

The future of HealThrive AI is filled with exciting opportunities. We are looking to collaborate with healthcare experts to enhance the accuracy of the advice the app provides, as this consultation can improve our AI model. Another ambitious step would be integrating it with wearable technology for real-time monitoring, prediction, and advice to users on a more comprehensive scale.
## Inspiration

We've all left a doctor's office feeling more confused than when we arrived. This common experience highlights a critical issue: over 80% of Americans say access to their complete health records is crucial, yet 63% lack their medical history and vaccination records since birth. Recognizing this gap, we developed our app to empower patients with real-time transcriptions of doctor visits, easy access to health records, and instant answers from our AI doctor avatar. Our goal is to ensure EVERYONE has the tools to manage their health confidently and effectively.

## What it does

Our app provides real-time transcription of doctor visits, easy access to personal health records, and an AI doctor for instant follow-up questions, empowering patients to manage their health effectively.

## How we built it

We used Node.js, Next.js, webRTC, React, Figma, Spline, Firebase, Gemini, Deepgram.

## Challenges we ran into

One of the primary challenges we faced was navigating the extensive documentation associated with new technologies. Learning to implement these tools effectively required us to read closely and understand how to integrate them in unique ways to ensure seamless functionality within our website. Balancing these complexities while maintaining a cohesive user experience tested our problem-solving skills and adaptability. Along the way, we struggled with Git and debugging.

## Accomplishments that we're proud of

Our proudest achievement is developing the AI avatar, as there was very little documentation available on how to build it. This project required us to navigate through various coding languages and integrate the demo effectively, which presented significant challenges. Overcoming these obstacles not only showcased our technical skills but also demonstrated our determination and creativity in bringing a unique feature to life within our application.

## What we learned

We learned the importance of breaking problems down into smaller, manageable pieces to construct something big and impactful. This approach not only made complex challenges more approachable but also fostered collaboration and innovation within our team. By focusing on individual components, we were able to create a cohesive and effective solution that truly enhances patient care. Also, learned a valuable lesson on the importance of sleep!

## What's next for MedicAI

With the AI medical industry projected to exceed $188 billion, we plan to scale our website to accommodate a growing number of users. Our next steps include partnering with hospitals to enhance patient access to our services, ensuring that individuals can seamlessly utilize our platform during their healthcare journey. By expanding our reach, we aim to empower more patients with the tools they need to manage their health effectively.
## Inspiration

As a patient in the United States you do not know what costs you are facing when you receive treatment at a hospital or if your insurance plan covers the expenses. Patients are faced with unexpected bills and left with expensive copayments. In some instances patients would pay less if they cover the expenses out of pocket instead of using their insurance plan.

## What it does

Healthiator provides patients with a comprehensive overview of medical procedures that they will need to undergo for their health condition and sums up the total costs of that treatment depending on which hospital they go-to, and if they pay the treatment out-of-pocket or through their insurance.
This allows patients to choose the most cost-effective treatment and understand the medical expenses they are facing. A second feature healthiator provides is that once patients receive their actual hospital bill they can claim inaccuracies. Healthiator helps patients with billing disputes by leveraging AI to handle the process of negotiating fair pricing.

## How we built it

We used a combination of Together.AI and Fetch.AI. We have several smart agents running in Fetch.AI each responsible for one of the features. For instance, we get the online and instant data from the hospitals (publicly available under the Good Faith act/law) about the prices and cash discounts using one agent and then use together.ai's API to integrate those information in the negotiation part.

## Ethics

The reason is that although our end purpose is to help people get medical treatment by reducing the fear of surprise bills and actually making healthcare more affordable, we are aware that any wrong suggestions or otherwise violations of the user's privacy have significant consequences. Giving the user as much information as possible while keeping away from making clinical suggestions and false/hallucinated information was the most challenging part in our work.

## Challenges we ran into

Finding actionable data from the hospitals was one of the most challenging parts as each hospital has their own format and assumptions and it was not straightforward at all how to integrate them all into a single database. Another challenge was making various APIs and third parties work together in time.

## Accomplishments that we're proud of

Solving a relevant social issue. Everyone we talked to has experienced the problem of not knowing the costs they're facing for different procedures at hospitals and if their insurance covers it. While it is an anxious process for everyone, this fact might prevent and delay a number of people from going to hospitals and getting the care that they urgently need. This might result in health conditions that could have had a better outcome if treated earlier.

## What we learned

How to work with convex fetch.api and together.api.

## What's next for Healthiator

As a next step, we want to set-up a database and take the medical costs directly from the files published by hospitals.