## Inspiration

Our inspiration stems from the profound impact that early disease detection can have on exponentially improving survival rates.

## What it does

Imagine a world where we can detect diseases early and save lives by treating them before they get out of control. According to the Canary Foundation, "Colon Cancer caught early has a 91% 5-year survival rate, vs. an only 11% survival rate if it is caught late and has spread to other organs." With this in mind, our hackathon project leverages cutting-edge medical wearable technology to empower doctors with real-time, lifesaving information. By gathering and transmitting data on vital signs, sleep patterns, and activity levels, our solution enables healthcare providers to detect warning signs before they escalate into critical conditions. This also eliminates miscommunication and misunderstandings between providers and patients, as the data is clear and concise. With early intervention, we can prevent the unnecessary loss of lives and significantly improve patient outcomes.

## How we built it

Software Developers - We created a Terra API account and following the documentation, created calls to the Terra API in the backend of our web app to centralize outgoing API calls. Then, we connected our front end to our backend to display the data from Terra API for users to see.

We built the front end using the Angular framework, TypeScript, HTML, and CSS, and used Node.js and JavaScript for our backend. We also utilized Twilio to allow for care providers to send messages directly to their patients, which required us to use Express.

UX Designer - We started by sketching out our ideas on paper with our developers, creating a basic structure, identifying discussion topics, and defining our mission. Then, we moved on to building a wireframe in Figma.

After finishing the wireframe, we reviewed it and realized that some things needed reordering to better match how we expected users to interact with it.

In our program, a "notice" is health data sent from a smartwatch or other health-related device via Terra API to our project, and it appears on a physician's homepage.

One significant change we made was to prioritize the provider's "feed" of patient notices because it's crucial to act quickly when there's a life-threatening issue. Below that, we added the option for physicians to send notices when they have non-urgent concerns and want to schedule a meeting with the patient.

Next to that, we included a section for patients, making it easy for them to access their vitals and health history when needed.

This is a big change from the original layout. We decided on this layout specifically after conducting user research with the teams around us who were willing to give us their feedback.

## Challenges we ran into

We ran into the issue that our AI model wasn't specifically trained and prepared for medical use, therefore, we weren't able to dive as deep into medical advice or help the physician further than just the data.

## Accomplishments that we're proud of

We are proud that we were able to successfully utilize all 3 API's in our project!

## What we learned

Our group had a first-time hacker, and she came in without knowing what an API was, and then she left knowing, not only what it was, but how to integrate one! We all together learned how to integrate the Open AI API and the Terra API. To more effectively foster communication between patients and their primary care providers, we used Twilio to send a text messages from physicians to patients to request appointments or calls based on their health data.

Teamwork was also something we improved on and made more efficient during this hackathon. we streamlined our process of working with a UX Designer and turning her designs into reality efficiently.

We also learned that we love the restaurant Veggie Galaxy on Mass Ave!!!!

## What's next for ApolloLink

We would love to upgrade our product to be able to offer advice or diagnostic examples. This, however, would need to be extensively tested and be created with a medically trained AI model.
## 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

Our inspiration were are friends who have Autism and also hearing the keynote speaker talk about her brother who is Autistic.

## What it does

Assist ASD is an application made for people with autism, their caretakers and behavioral therapists. Our mobile application allows people with autism to understand social signals through image/facial recognition. In addition, the care takers are able to track their receiver in order to know events like when entering and leaving home, sleeping schedules and physical activity. Care takers not only track these events, but have measures for recording notes based ABC assessment. Lastly, all information and data is then pushed to the behavioral therapist who is able to view the metrics and data analytics in order to make more informed decisions for their patients.

## How I built it

Using AGILE/SCRUM planning, we built it using technologies based on scope. After planning the project, we incorporated google's cloud service and facial recognition along with Microsoft's sentimental analytics. We incorporated Neura's API in order to get updates on the autistic person's movements such as physical activity, sleeping activity and entering or leaving designated points. We used Qualtrics in order to display data analytics of all behaviors tracked.

## Challenges I ran into

We ran into API issues with Microsoft services. Initially it was a very confusing setup because they only had code for objective C. However, after playing around with the curl and python aspect it became useful. We also had an interesting adventure of scoping our project more precisely and cutting out possible technologies that we could use.

## Accomplishments that I'm proud of

Well, we see our application having a big impact on the ASD community.

## What I learned

More about the problem space of ASD and new technologies and capabilities for applications.

## What's next for Assist ASD

We will user interview behavioral therapist to make sure we are solving and implementing the right features.