The study utilized over 20 hours of hospital-recorded heart sounds from a publicly available dataset, creating 23,381 five-second heart sound snippets for analysis.12 Researchers discovered that mid-level layers in AI models were most effective at detecting heart signals, while deeper layers typically optimized for speech recognition proved less suitable for analyzing biological sounds.1 This finding suggests that Apple would need to focus on specific components of their AI architecture rather than using complete speech models for health tracking applications.

Six popular foundation models were tested in the research, including HuBERT, wav2vec2, wavLM, Whisper, and Apple's internally developed CLAP model.12 Despite not being specifically trained for healthcare applications, these models matched or exceeded traditional methods that rely on handcrafted audio features, demonstrating the versatility of general-purpose AI systems when applied to specialized medical tasks.1

The standout performer in Apple's heart rate monitoring research was their in-house CLAP (Contrastive Language-Audio Pretraining) model, which achieved remarkable accuracy in estimating heart rates from audio recordings. This proprietary model outperformed other tested foundation models, delivering a precision level comparable to dedicated medical devices.12 The CLAP model's exceptional performance suggests Apple has developed specialized audio processing capabilities that could be particularly effective for health monitoring applications.

When processing phonocardiograms, the model demonstrated potential to achieve up to 98.7% accuracy in heart rate monitoring according to some reports.1 This level of precision positions AirPods as potential competitors to dedicated health monitoring devices, offering a significant advantage since they would utilize hardware already owned by millions of users worldwide rather than requiring the purchase of specialized equipment.32

The existing microphones in AirPods could capture heart sounds while AI processes the data in the background, enabling passive health monitoring without additional user effort. This seamless integration would make heart rate tracking as simple as wearing AirPods during daily activities—whether exercising, commuting, or listening to music.12 The technology could potentially detect irregularities like atrial fibrillation, similar to how the Apple Watch currently provides ECG monitoring and irregular rhythm notifications.13

Unlike dedicated health devices that require conscious engagement, this approach would transform a device primarily used for audio entertainment into a dual-purpose health monitor. For the millions of users who don't own an Apple Watch but regularly use AirPods, this innovation could democratize access to sophisticated health tracking features through hardware they already possess.14 Recent speculation suggests the upcoming AirPods Pro 3 might be Apple's first model to incorporate this technology.56

Building on its extensive health initiatives, Apple's exploration of AI-powered heart monitoring through AirPods aligns with what Bloomberg described as the company's "biggest push into health yet."1 The Apple Heart and Movement Study, involving over 140,000 participants, has already demonstrated how wearables can effectively track cardiovascular health and physical activity.12 This research foundation provides a natural progression path for implementing heart monitoring features in future AirPods models.

The potential health applications extend beyond simple heart rate monitoring to detecting serious conditions like atrial fibrillation, which has been successfully implemented in Apple Watch.3 Studies have shown that early detection of such arrhythmias through wearable technology can lead to significant symptom relief following treatment and may prevent development of heart failure.3 For regulatory approval, Apple would likely need to conduct extensive clinical validation similar to previous studies that established the effectiveness of smartwatch-based ECG monitoring.45