## Inspiration

Due to the outbreak of COVID-19, non-urgent healthcare services had reduced drastically and there is an increasing demand for remote outpatient solutions.

Current telemedicine apps help to connect patients to doctors, however, not all patients have access to these technologies due to physical impairments.

In light of this, we would like to build a symptoms reporter to tackle the digital divide by allowing visually impaired patients to report their symptoms through Google assistant, a voice-enabled AI software that leverages machine learning and natural language processing (NLP), and visualizing the health data collected for healthcare providers

## What it does

The app collects personal information such as name and date of birth to help healthcare providers to identify the patient, and record the details of symptoms reported by patients.

## How we built it

* Conversational design using voiceflow
* Deploy via Google Actions Console
* Database using Firebase

## Challenges we ran into

None of us has used voiceflow before, we watched the whole series of voiceflow tutorial videos and built the app from scratch.

We encountered a bunch of bugs during the process of conversational design (hard to predict how humans react lol), and gone through a long process of debugging.

## Accomplishments that we're proud of

We built a functional app!

## What we learned

We learnt a lot about conversational deisgn and using voiceflow to build a voice app!

## What's next for ReportDEX

* book appointments
* support other platforms (e.g. Alexa)
* support offline (e.g. SMS/ phone call)
* screen for keywords that indicates the severity & schedule an appointment when keywords detected
## Inspiration

We were inspired by the need to provide patients with a reliable tool that can simplify the process of online diagnosis. Too often, when we Google our symptoms, we're met with exaggerated results that make it seem like we’re facing life-threatening conditions. Our web app combines AI with real time data from Groq to create accurate and accessible solutions to the diagnosis without unnecessary panic.

## What it does

Our web app takes patient symptoms, synthesizes data using Fetch AI and Groq to provide real time diagnosis based on patient symptoms. Llama Guard ensures that the data is handled securely and protects sensitive information maintaining user privacy.

## How we built it

We used Fetch AI to have multi-agent communication to talk to each other, reflex for a user-friendly interface and Groq as a large language model to process and generate the potential diagnosis. Python served as the backbone of our app organizing the data flows between Fetch AI, Reflex and Groq while also handling backend logic for symptom processing and diagnosis generation. Llama Guard was integrated to ensure secure data management.

## Challenges we ran into

One of the challenges we faced was integrating Fetch.AI to process increasingly complex and case-specific data. We were able to overcome the challenge by using a bureau of local agents to simulate greedy linear search to efficiently narrow down a patient's ailment without an enormous space complexity. We also tapped into the vast resources of the Agentverse to enable user input, Groq interfacing, web scraping, summarization, diagnosis, and recommendation. It was also challenging to ensure Groq’s AI could deliver accurate diagnosis results. Balancing the need for user-friendly design with the complexity of medical data was another challenge.

## Accomplishments that we're proud of

We are proud of successfully integrating Fetch AI with verified websites like WebMD's data. Another accomplishment was the effective implementation of Groq's large language model, which provided reliable and precise diagnosis predictions. Ensuring that the AI could interpret and analyze symptom data without overwhelming or misinforming users was a critical milestone. We are proud of creating a secure environment through the integration of Llama Guard. This ensured not giving any misinformation or unnecessary panic to patients.

## What we learned

We learned a lot about using AI in healthcare applications, from securing data with Llama Guard to synthesizing real-time medical data. The project also fortified the importance of user-centered design in creating tools that patients trust and feel comfortable using.

## What's next for Pocketdoc

We are excited to hear back from our mentors and talk to actual health professionals to make sure Pocketdoc complies with health regulations. We also aim to introduce features like multilingual support, and deeper integration with wearable devices for real-time health monitoring.
## Story

Mental health is a major issue especially on college campuses. The two main challenges are diagnosis and treatment.

### Diagnosis

Existing mental health apps require the use to proactively input their mood, their thoughts, and concerns. With these apps, it's easy to hide their true feelings.

We wanted to find a better solution using machine learning. Mira uses visual emotion detection and sentiment analysis to determine how they're really feeling.

At the same time, we wanted to use an everyday household object to make it accessible to everyone.

### Treatment

Mira focuses on being engaging and keeping track of their emotional state. She allows them to see their emotional state and history, and then analyze why they're feeling that way using the journal.

## Technical Details

### Alexa

The user's speech is being heard by the Amazon Alexa, which parses the speech and passes it to a backend server. Alexa listens to the user's descriptions of their day, or if they have anything on their mind, and responds with encouraging responses matching the user's speech.

### IBM Watson/Bluemix

The speech from Alexa is being read to IBM Watson which performs sentiment analysis on the speech to see how the user is actively feeling from their text.

### Google App Engine

The backend server is being hosted entirely on Google App Engine. This facilitates the connections with the Google Cloud Vision API and makes deployment easier. We also used Google Datastore to store all of the user's journal messages so they can see their past thoughts.

### Google Vision Machine Learning

We take photos using a camera built into the mirror. The photos are then sent to the Vision ML API, which finds the user's face and gets the user's emotions from each photo. They're then stored directly into Google Datastore which integrates well with Google App Engine

### Data Visualization

Each user can visualize their mental history through a series of graphs. The graphs are each color-coded to certain emotional states (Ex. Red - Anger, Yellow - Joy). They can then follow their emotional states through those time periods and reflect on their actions, or thoughts in the mood journal.