Hillo develops an AI-driven platform that provides personalized management tools for diabetic patients, utilizing machine learning algorithms to analyze glucose data and dietary habits. This technology helps users maintain optimal blood sugar levels and improve their overall health outcomes.
Funding
$3.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Managing diabetes effectively requires continuous monitoring of blood glucose levels and personalized adjustments to treatment plans, which can be challenging for both patients and healthcare providers. Patients often struggle to understand the impact of lifestyle choices on their glucose levels, while healthcare providers face difficulties in efficiently analyzing patient data to provide tailored recommendations.
Solution
Hillo AI offers a digital twin platform that leverages AI to simulate and predict blood glucose responses in diabetic patients, enabling personalized diabetes management. By creating a digital replica of a patient's glycemic response, the platform anticipates how changes in lifestyle or treatment plans might impact blood glucose control. For healthcare providers, Hillo AI extracts behavioral patterns and therapeutic habits to understand the root causes of glycemic excursions and simulates the impact of treatment adjustments. For patients, the platform integrates with logbook apps to provide real-time blood glucose predictions and personalized recommendations, empowering them to make informed decisions and prevent glycemic excursions.
Target Audience
The primary target audience includes healthcare providers (HCPs) specializing in diabetes care and diabetic patients seeking personalized management tools to improve blood sugar control and overall health outcomes.
Features
- AI-powered digital twins that mimic individual glycemic responses based on real-world patient data.
- Simulation and prediction capabilities to anticipate the impact of lifestyle and treatment changes on blood glucose levels.
- Algorithms for missing meal data imputation, automatic data tagging, and extraction of injection habits.
- Glycemic pattern analysis to identify behavioral patterns contributing to glycemic excursions.
- Real-time blood glucose prediction integrated into a logbook app for personalized recommendations.
- Clinically validated AI technology with prediction accuracy up to 99.9% within Parkes error grid A+B zones at 30-minute horizons.
- Web application for healthcare providers to streamline data analysis and treatment adaptation.