Key result
AI-enabled stethoscope fails to increase new HF diagnoses compared to routine care.
Why the study?
Early detection of cardiovascular disease is a global public health priority, and AI-enabled stethoscopes offer robust point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease, but their real-world effect and implementation challenges were unknown.
Does the implementation of an AI-enabled stethoscope in routine primary care increase the detection of heart failure compared to routine care?
RCT (n=1,553,175)
Open-label
1:1 cluster
Yes
Does the implementation of an AI-enabled stethoscope in routine primary care increase the detection of heart failure compared to routine care?
The implementation of an AI-enabled stethoscope in routine primary care did not significantly increase the overall detection of heart failure or shift diagnoses to the community setting over 12 months.
Answers are grounded in this paper and cite their sources.
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“This is an elegant example of how the humble stethoscope, invented more than 200 years ago, can be upgraded for the 21st century. By helping GPs identify heart disease in seconds, this technology offers patients earlier access to life-saving treatment, often before symptoms become severe. Demonstrating the benefits of real-world implementation of Eko's device is the real impact of the TRICORDER programme and the mission of The Health Impact Lab.”
“Some practices used the device less over time because it added extra steps to routine care and was not well integrated with existing electronic health record systems. Despite its diagnostic accuracy, it was these practical barriers that limited the overall impact of the technology.”
“Studies like this are essential for patients, health systems, industry, regulators and researchers to bring the most promising health innovations into routine patient care.”
Requires proper integration into primary care for earlier CVD detection; extends traditional stethoscope utility with randomised AI evidence.
Summary Background Early detection of cardiovascular disease is a global public health priority. Artificial intelligence (AI)-enabled stethoscopes offer robust performance characteristics in point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease (VHD). We conducted a pragmatic, cluster-randomised controlled implementation trial to determine the real-world effect and implementation challenges of AI-stethoscopes. Methods UK primary care practices were cluster randomised 1:1 to intervention (training and implementation in use of AI-stethoscopes in routine care) or control (routine care). Given the nature of the intervention, masking of participants (practices, clinicians, and patients) was not feasible. During cardiac examinations, the AI stethoscope recorded 15 s of single-lead electrocardiogram and phonocardiogram signals for input to three AI algorithms that returned binary predictions for the presence or absence of reduced left ventricular ejection fraction (≤40%), atrial fibrillation, and VHD (all with regulatory approval). The primary endpoint was incidence of any newly coded diagnosis of heart failure (all subtypes), expressed per 1000 patient-years (incidence rate ratio [IRR]), derived from a UK National Health Service Secure Data Environment. A coprimary endpoint stratified detection of heart failure by place of diagnosis (community-based vs via hospital admission). Secondary endpoints included atrial fibrillation and VHD detection rates, performance characteristics of the AI-stethoscope, use rates, and clinician-reported implementation barriers and enablers. Findings Between Oct 30, 2023, and May 22, 2024, 205 practices were randomly assigned (96 to the intervention arm [701 933 registered patients] and 109 to the control arm [851 242 registered patients]). Intervention practices recorded 12 725 patient examinations with the AI-stethoscope, across 972 clinical users. Intention-to-treat analysis found heart failure detection did not differ between groups (IRR 0·94 [95% CI 0·86–1·02]); with no difference in community-based or hospital-based diagnoses (p>0·05). Interpretation Implementation of an AI stethoscope in routine primary care did not significantly increase detection of heart failure or increase community-based diagnosis after 12 months of implementation. AI stethoscope use was independently associated with significantly higher detection rates of heart failure, as well as atrial fibrillation and VHD. This randomised controlled implementation trial establishes a pragmatic design with randomisation that generates real-world data essential for understanding and overcoming the barriers to implementation of innovation in health care. Funding National Institute for Health and Care Research, British Heart Foundation, and Imperial Health Charity.
No takes yet. Share an insight, caveat, or question.
Kelshiker et al. (2026) conducted an RCT in Cardiovascular disease (n=1,553,175). AI-enabled stethoscope vs. Routine care was evaluated on Incidence of any newly coded diagnosis of heart failure (all subtypes) (IRR 0.94, 95% CI 0.86-1.02). Implementation of an AI-enabled stethoscope in routine primary care did not significantly increase the incidence of newly diagnosed heart failure compared to routine care (IRR 0.94; 95% CI 0.86-1.02).
Synapse has enriched 10 related papers. Consider them for comparative context: