Editing AI for Wearable Health Devices (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Wearable AI Diagnostic Performance
! Application !! Device !! Sensitivity !! Specificity !! FDA Status
|-
| AFib detection (PPG) || Apple Watch || 84% PPV || ~99% (low false alarm) || Cleared
|-
| AFib detection (ECG patch) || AliveCor || 98% || 97% || Cleared
|-
| Sleep staging (4-class) || Consumer wearable || ~70% || ~85% || Not cleared (wellness)
|-
| Hypoglycemia prediction (CGM) || Dexcom G7 || ~80-90% (30min ahead) || ~90% || Cleared (CGM)
|-
| Fall detection || Apple Watch || ~80% || ~97% || Cleared
|-
| SpO2 monitoring || Various || 90-96% vs. pulse ox || — || Emergency only
|}

'''Failure modes''': Motion artifacts — exercise, typing, and everyday movement corrupt PPG/ECG signals. Skin tone bias — PPG sensors perform less accurately on darker skin tones due to melanin light absorption. Device heterogeneity — algorithms developed on one device fail on another. Battery life / wear compliance — gaps in continuous monitoring create blind spots. Alert fatigue — too many false-positive notifications lead users to disable health features.
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