Google AI spots high blood pressure using ordinary watch sensors
Google researchers and medical collaborators have used ordinary smartwatch signals to flag possible high blood pressure without calibrating the watch against an arm cuff.
A study published in Nature on Nov. 5 suggests a way to prompt clinical checks during everyday wear, although the system missed about 33% of hypertension cases.
The AI identified 65.8% of participants with hypertension according to a clinical reference test (sensitivity), while it correctly screened 90% of those without the condition as negative (specificity).
The prospective, multicenter study followed 196 participants through seven days of smartwatch monitoring and compared results with 24-hour ambulatory blood-pressure monitoring. Participants had no known cardiovascular disease and were not taking blood-pressure medication, limiting how far the findings extend to people already receiving treatment.
The 95% confidence intervals were 54% to 76.3% for sensitivity and 83.2% to 94.7% for specificity.
Two groups, two screening measures
Two groups, two screening measuresWith hypertensionWithout hypertension
Of participants with hypertension on 24-hour ambulatory monitoring, 65.8% were detected and 34.2% were missed.
Participants with hypertension65.8% detected; 34.2% missed. These are different denominator groups, not the overall population. PARTICIPANTS WITH HYPERTENSION 65.8%34.2% DetectedMissed Sensitivity: 65.8% | 95% CI: 54.0% to 76.3% Missed share derived as 100% – 65.8%.
Participants with hypertension65.8% detected; 34.2% missed. These shares use the reference test to define the group.WITH HYPERTENSION 65.8%34.2% DetectedMissed Sensitivity: 65.8% 95% CI: 54.0% to 76.3%Missed share = 100% – 65.8%
Of participants without hypertension on the reference test, 90.0% screened negative and 10.0% produced false alerts.
Participants without hypertension90.0% correctly negative; 10.0% false alerts. These are different denominator groups, not the overall population. PARTICIPANTS WITHOUT HYPERTENSION 90.0%10.0% Correctly negativeFalse alerts Specificity: 90.0% | 95% CI: 83.2% to 94.7% False-alert share derived as 100% – 90.0%.
Participants without hypertension90.0% correctly negative; 10.0% false alerts. These shares use the reference test to define the group.WITHOUT HYPERTENSION 90.0%10.0% Correctly negativeFalse alerts Specificity: 90.0% 95% CI: 83.2% to 94.7%False-alert share = 100% – 90.0%
Screening can flag a reason for a clinical check; a negative result cannot exclude hypertension.
196 participants in a selected, risk-enriched cohort, without known cardiovascular disease or antihypertensive medication. Bars show conditional study rates, not predictive values for all smartwatch users. This is screening research, not a standalone diagnosis.
How everyday sensors become a screening signal
The system, called AI-PPG-ACC-HTN, combines optical pulse-wave measurements with accelerometer signals.
According to Google Research’s explanation, it aggregates those signals across days and combines them with the user’s height to produce a probability of hypertension.
It does not report an exact systolic and diastolic blood-pressure reading. No personal cuff reading is needed to calibrate the smartwatch system, though cuff-based measurements remained the reference the researchers used to test it.
The waveform model was pretrained using 492,347 hours of unlabeled sensor data from 2,109 consenting participants.
The study supplement describes the prospective cohort as intentionally risk-enriched. In this selected group, 80.6% of positive smartwatch screens matched hypertension on the reference test, the measure known as positive predictive value.
The research involved Google Research, Beth Israel Deaconess Medical Center and Harvard Medical School.
The authors describe a possible role for smartwatch screening in prompting formal assessment and confirmatory diagnosis using established cuff-based methods, with the watch prompting a clinical check.
