Apple’s Wearable AI Detects Pregnancy With Over 90% Accuracy
Rohit Mahajan
Wearables and the Internet of Things (IoT) have been integral to the way that Generative AI is transforming healthcare. Apple’s latest contribution to this revolution comes in the form of a model that can detect pregnancy with 92% accuracy by analyzing behavioral data from iPhones and Apple Watches.
The findings are detailed in a study titled “Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions.” This study introduces the Wearable Behavior Model (WBM), which relies on higher-level health metrics—like sleep quality, mobility, and heart rate variability—rather than just raw sensor data, which can often be noisy and hard to interpret.
The WBM was trained using over 2.5 billion hours of wearable data and outperformed older models that relied on low-level sensor inputs. Researchers built a pregnancy dataset from 430 pregnancies, using Apple Health, HealthKit, and photoplethysmography (PPG) data. They labelled the nine months leading to childbirth and one month after as “positive” weeks—representing active pregnancy or postpartum recovery—while other weeks were marked “negative.”
The research comes from the Apple Heart and Movement Study, which collected over 15 billion data points from more than 162,000 participants. The data came through the everyday use of the Apple Watch and iPhone. For the pregnancy research, the model analyzed information from 430 reported pregnancies and more than 25,000 non-pregnant participants. The AI looked at more than heart rate and temperature. It also examined movement patterns, sleep habits, and exercise routines.
One of the key advancements of WBM is its use of expert-designed algorithms to convert raw sensor readings into meaningful behavioral metrics. These metrics are not only clearer but also better aligned with real health states.
The model allows for more precise tracking and prediction of health changes over time, marking a significant step forward in wearable-based health monitoring and AI-powered diagnostics.
Pregnancy was just one of several health conditions the WBM model learned to identify. The researchers also tested the model on other health issues with strong results. It predicted diabetes with 82% accuracy, infection with 76% accuracy, and injury with 69% accuracy. These findings suggest that AI-powered wearables may soon do much more than count steps or track sleep. They could help detect serious health changes before symptoms even appear.
Even with these promising results, trust remains a major barrier in women’s health technology. Privacy concerns are growing, especially when it comes to sensitive data like menstrual cycles or pregnancy. In 2023, the Federal Trade Commission fined the popular app Premom for sharing user data without consent.
A recent FTC study confirmed growing skepticism. Women are less likely to trust apps that collect reproductive health information, especially when the companies do not make their data practices clear. Even if the Apple Watch can detect early signs of pregnancy, would users want it to? This is a key question, one that echoes privacy and ethical concerns across the board as generative AI becomes more ubiquitous in healthcare. Because of such concerns, Apple has not announced any plans to turn the research findings into a consumer feature. But this research shows where Apple’s focus may be headed. With support from public health officials calling for widespread use of wearables, Apple could play a key role in shaping the future of personalized healthcare.
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