GluCozie
Challenge:
To assist ongoing glucose level awareness in a non-clinical setting.
Solution:
To provide an unobtrusive manner to detect glucose level trends.
Features
Non-Clinical Users
Glucose Trend Detection
Wireless
AI Models
Convolutional Neural Network
Variance Auto-Encoder
User Experience
Customer Journey
Mobile App
Web Dashboard
Interaction
Challenge
The customer will become aware of their glucose trending through multiple devices.
While the primary use case is the interaction with a phone, a web-based dashboard is available to help with greater detail.
.
Phone App
The primary interaction for customers is to become aware of their current trending glucose levels. The phone will update in real-time and can update through wi-fi. No mobile network is necessary.
Dashboard
A web-based dashboard will show how trends are seen across time.
User Journey
The journey map shows the most critical peer pathways.
The purpose is to reach a cohesive truth, and the journey map is a way to show the level of consistency within a product experience. It helps us understand the product on a high level while informing us where we can improve in the future product roadmap.
Also, it is an excellent way for user experience intention to continue to encourage understanding among the key stakeholders. It can also add clarity to business.
Product Experience
Components work together in concert, so the benefit is seamless and intuitive.
Our customers have comparable stories. They had often occupied a clinical facility or a hospital. Becoming a customer of GluCozie can be the beginning of a different lifestyle. Our customers are ready to return to their lives, and so it is a priority to forge a practical solution that is compatible with their newfound changes. We want to help integrate unfamiliar needs with their new lifestyle.
MIT xPRO
AI in Healthcare Fundamentals & Applications
GluCozie was initially the thesis for the program. Since then, it has taken on a life of its own and has been progressing into the commercial space. The design and AI work shown here were necessary for a product to prove itself in the market.
The need for an enterprise-class user experience is valuable. This was also an accurate AI solution pipeline that was enhanced by the relevant training.
Built on Existing Models
As part of the requirements, we were tasked with leveraging solutions through a preexisting AI platform. This served to be a boon since much of the question of the AI’s potential was answered already, and the focus was on the innovation in the service.
A New Market
Originally, conceived as a clinical device, a challenge revealed itself regarding the limitations in the AI's ability to detect glucose levels. Specifically, the error range was too great to attain compliance in the US market. Hence, the business development decision is to seek post-clinical and pre-diabetic individuals to be aware of how their personal glucose levels are trending.
The future of GluCozie is nuanced since much of the design work could be suited as a component to existing bioscience products.
Pipeline validated by Microsoft Copilot (GPT-5). Hot Air Balloon FPO grahic was rendered by Microsoft Copilot (GPT-4).
Elder woman was generated by FireFly 3
Features
Wireless
Glucose Trend Detection
Non-Clinical Users
AI Models
Convolutional Neural Network
Variance Auto-Encoder
User Experience
Customer Journey
Mobile App
Web Dashboard