Google and Fitbit Are Building a Personalized Health LLM
Representative image. Source: Fitbit

Google and Fitbit Are Building a Personalized Health LLM

Leveraging Google’s family of generative AI models in Gemini and health data from Fitbit and Pixel devices, this initiative aims to provide tailored guidance based on individual health and fitness objectives.

Google Research and Fitbit are building a personal health LLM to offer personalized coaching and recommendations through a mobile app. Leveraging Google’s family of generative AI models in Gemini and sufficient health data from Fitbit and Pixel devices, this initiative aims to provide tailored guidance based on individual health and fitness objectives.

Google claims that through meticulous fine-tuning, the model is being optimized to deliver insightful and actionable messages based on users' specific health profiles. So, a diverse range of carefully selected health data from research studies ensures the model's robust reasoning capabilities. 

“This model is being built on Gemini models and fine-tuned on a de-identified, diverse set of health signals from high-quality research case studies. The studies are being collected and validated in partnership with accredited coaches and wellness experts, enabling the model to exhibit profound reasoning capabilities on physiological and behavioral data,” reads an official Google blog.

In January 2021, Google acquired Fitbit for $2.1 billion, rebranding it as Google Fitbit. Since then, Google has integrated the fitness-tracking company into its hardware portfolio. The fitness and smartwatches brand boasts over 120 million active users.

Earlier this year, Google introduced AMIE (Articulate Medical Intelligence Explorer), a research AI system built on an LLM and optimized for diagnostic reasoning and clinical conversations.

“We explored the performance of an LLM by simulating text-based consultations with patient actors, adapting a well-known framework of ‘Objective Structured Clinical Examinations’ to a consumer-facing user interface. In a randomized comparison with real primary care clinicians performing the same simulated text consultations, appropriately trained LLM rated higher than or on par with these consultations when measured for traits like diagnostic accuracy, empathy, and helpful explanation,” the blog further adds.

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