Saving lives with AI health coaching

# Saving lives with AI health coaching Source: [https://openai.com/index/healthify/](https://openai.com/index/healthify/) Healthify has been pioneering using AI for driving behavior change in health & fitness for over a decade\. By 2018, Healthify already had more than 5 million users and hundreds of nutritionists and trainers exchanging millions of messages with their clients each month, along with tens of thousands of hours of calls and meal & fitness plans each month\. Healthify’s data pipeline inherently also came with feedback loops—with knowledge of which messages and plans created higher engagement and impact\. Using this wealth of real\-world, contextual information, Healthify made significant strides in AI, notably with the launch of Ria, the world's first AI\-powered virtual nutritionist, and Coach Co\-pilot—its coach\-facing assistant\. Ria utilized hierarchical LSTMs \(Long Short\-Term Memory\) and custom NLU \(Natural Language Understanding\) systems to accurately recognize user intents and provide relevant answers\. By 2020, Ria was handling the majority of user messages directly\. Combined with the Coach Co\-pilot, this breakthrough allowed coaches to significantly scale their services, managing up to 300 clients simultaneously, while hitting record high NPS through improvements to personalized health coaching\. In 2021, Healthify also introduced Snap, a revolutionary feature designed to simplify calorie counting through photo recognition of food\. Snap utilized Convolutional Neural Networks \(CNNs\) and proprietary models to accurately recognize single food items, particularly focusing on Indian cuisine\. This technology not only respected user privacy but also tailored food recommendations by incorporating user\-specific contexts\. Over time, Snap achieved around 80% accuracy for single Indian foods\. Despite these successes, Healthify encountered challenges: - **Performance**\. It took multiple iterations to get Snap to recognize food accurately, and it struggled when photos contained multiple foods\. As a result, “Snap was only used 10–20% of the time,” said Healthify CEO[Tushar Vashisht⁠\(opens in a new window\)](https://www.linkedin.com/in/tusharvashisht)\. Similarly, Ria was rules\-based, so it couldn’t truly answer the long tail of valuable yet complex queries about nutrition \(e\.g\., “how did my food yesterday affect my sleep last night?”\)\. - **Scale\.**For each new country they wanted to add, Healthify needed to spend lots of effort on localizing models for the language, common foods, and exercise routines\. “It took us two years to enter Southeast Asia,” Vashisht explained\. The collaboration with OpenAI emerged as a pivotal solution to overcome these limitations, significantly advancing Healthify’s offerings and setting a new standard for innovation in the health technology sector\.