**Introduction/Hypothesis**
Recently, an artificial intelligence (AI) algorithm was validated for identifying ECG signatures of atrial fibrillation (AF) risk during normal sinus rhythm. Traditionally, socioeconomically disadvantaged populations are less likely to receive novel or even guideline-recommended treatments, and have high chronic disease burden, including cardiovascular diseases. There have also been concerns about the new technologies creating a digital divide, worsening existing health disparities. Our study examined the effect of an AI algorithm across patients with different levels of social determinants of health (SDOH) using prior pragmatic study.

**Methods**
The initial prospective, non-randomized, interventional study leveraged EHR and digital technologies to prospectively enroll 1004 patients with a broad range of AI-ECG risks of AF, who received continuous cardiac rhythm monitoring for up to 30 days. The Area Deprivation Index (ADI) was used as a composite measure of SDOH which is comprised of indicators spanning four domains: income, education, employment, and house quality was determined for each patient. In total, 904 intervention patients linked to the ADI data were matched to 904 real-world control patients derived from the eligible but unenrolled population. Patients with ADI under 33% were defined as having better SODH; and patients with ADI above 33% were defined as having worse SODH.

**Results**
The median age 74 years, 38% were women. After matching, the intervention and control arms were balanced in all baseline characteristics, including HF, HTN, diabetes, stroke/TIA, CAD/PAD, and ADI. A hazard ratio of 3.11 (P&lt;0.001) was computed when comparing intervention to control for primary AF diagnosis outcome. In patients with worse SODH, the intervention increased AF detection from 2.6% to 8.8% (HR 3.63 [2.09, 6.32]); in the better SODH, the intervention increased from 3.9% to 9.3% (2.41 [1.23, 4.74].

**Conclusion**
An AI-guided AF screening program increased the detection of AF in both patients with better or worse SODH. Because AF is more likely to be underdiagnosed in patients with worse SODH, the current study validates the use of AI in historically disadvantaged populations displaying the possibility for broad implementation with decreased possibility of perpetuating disparities.



AI-Guided Screening Increased AF Detection among Patients with Different Socioeconomic Status