aceFuture promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease
Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. Methods For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or…
Systematic review found most ML algorithms improved CVD risk prediction compared with Framingham Risk Score by incorporating sociodemographic predictors and modeling non-linear interactions.
Some algorithms overestimated the number at risk versus FRS without addressing overdiagnosis risk, while treating statistical significance as clinical significance.
Review notes remaining gaps for patient benefit including need for clinical insight, adherence to screening principles, and cost-benefit assessment, plus inconsistent definitions of inputs and outcomes.
Evidence
- Peer-reviewedOpen Heart2026-09-12
How should this claim be treated?
Truvace Impact Record TRV-2026-1081, v1: “Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.” Truvace, 2026-09-14. /record/TRV-2026-1081 (accessed at citation time). sha256 fb230d9376b9e264…
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