How We Use AI for Your Health | Made by Google Podcast S9E6

On this episode of the Made by Google Podcast, host Rachid Finge sits down with Andy Abramson, Head of Product for Fitbit and Health, to explore the design and engineering behind Google’s newest health tools: the redesigned Google Health app, the screenless Fitbit Air, and the Gemini-powered Google Health Coach.

Andy explains why Google is returning to Fitbit’s screenless roots, how the team reacted when Reddit users tried to make their own custom bands, and snapping a few photos can go a long way in keeping your fitness plan on track, thanks to Gemini.

Based on the discussion with Andy Abramson, users play a crucial role in shaping product design and engineering at Google. Here is how they contribute to the improvement of health tools like the Fitbit Air and the Google Health Coach:

• Participating in User Research: The team relies heavily on user research, where they share mockups and prototypes with real people early in the development process to ensure they are on the right track (3:15).
• Testing and Feedback Loops: Before a product launch, thousands of users test the devices to uncover unique, unexpected questions and use cases that internal teams might miss (9:24–9:45).
• Surfacing Creativity & Community Trends: Google actively monitors platforms like Reddit, X, and Instagram to spot how users are hacking or customizing products. For example, when they saw users building custom bands for the Fitbit, the team released the official technical specifications within a week to support that community momentum (6:26–7:15).
• Collaborative Development: Through programs like “Insights Explorer,” users have been invited to try out labs and previews of early AI features, providing “unvarnished feedback” that helps developers solve complex problems (12:06–12:51).
• Providing Transparent Roadmaps: The team believes in a two-way conversation. By sharing public roadmaps and near-term improvement lists, Google acknowledges user input and demonstrates how it informs future updates (17:33–18:18).

 

• Conversational Workout & Activity Logging: Instead of manually navigating menus, users can talk to their devices. AI processes your spoken logs—such as sets and reps—and translates them directly into an organized workout tracker.
• Enhanced Coaching Experiences: By accurately parsing conversational workout logs, AI creates better context for trainers and coaches. This solves the common issue of wearable wrist devices being unable to measure exact weights lifted or comprehensive exercise data.
• Personalized Health Tracking: Wearable devices and phone sensors collect vast amounts of biopsychosocial data. AI models analyze this data (like sleep, movement, and physiology) to give you a holistic picture of your daily well-being.
• User Data Privacy and Security: A core priority for Google is ensuring that personal health information remains private, with robust security and data governance underpinning all AI health features. [4, 5, 6]

• Medical AI Models: Google’s MedLM and MedGemini are purpose-built models to assist doctors with clinical documentation, diagnostics, and patient interaction.
• AI Scribes: Voice-to-text algorithms are used in clinical settings to listen to doctor-patient interactions and generate draft medical notes, allowing physicians to focus more on patient care.
• Disease Detection: AI is trained to analyze medical imagery (like retinal scans for diabetic retinopathy) and sounds (such as coughs) to flag abnormalities earlier than traditional screenings. [10]