What question did this study set out to answer?

This work aims to address the cardiovascular disease risk assessment gap for women through non-invasive, AI-driven approaches.

April 22, 2026Open Access

AI-Driven Non-Invasive Cardiac Risk Assessment in Women: A Technical Review and Conceptual Framework

Key Result

An AI-driven multimodal framework integrating atypical symptoms and psychological factors is proposed to improve early-stage non-invasive cardiac risk assessment in women.

Key Points

This work aims to address the cardiovascular disease risk assessment gap for women through non-invasive, AI-driven approaches.
Reviewed existing AI applications in cardiovascular risk prediction.
Proposed a multimodal learning framework incorporating symptom inputs and psychological factors.
Highlighted interpretability mechanisms like SHAP and LIME for model transparency.
Identified limitations in current approaches to atypical symptoms in women.
Proposed a structured framework for integrating demographic and physiological data in risk assessment.
Outlined a phased validation pathway for practical deployment in clinical settings.

Structured PICO

Population

Women with mild or ambiguous chest discomfort (conceptual framework, no patient cohort)

Intervention

AI-assisted non-invasive screening approach combining symptom inputs, demographic attributes, and physiological signals

This technical review proposes a conceptual, clinically sensitive AI framework for early-stage cardiac risk awareness in women, addressing the gap in evaluating atypical symptoms.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Cardiovascular disease continues to be the leading cause of mortality worldwide, yet itsmanifestation in women remains insufficiently understood and frequently under diagnosed1,5. A particularly challenging aspect lies in the interpretation of mild or ambiguous chestdiscomfort, which often leads to anxiety-driven hospital visits or, conversely, dangerousdelays in seeking care. This paper explores the possibility of addressing this gap through anon-invasive, AI-assisted screening approach designed specifically with women’s symptomprofiles in mind. Rather than proposing a purely diagnostic tool, this work positions itself as aconceptual and technical bridge between symptom awareness and clinical intervention.The paper reviews existing artificial intelligence applications in cardiovascular risk predictionand identifies a critical limitation: the lack of integration of atypical symptoms andpsychological factors such as anxiety 6,7. Building on this observation, a structuredframework is proposed that combines symptom inputs, demographic attributes, and optionalphysiological signals within a multimodal learning architecture. Particular emphasis is placedon interpretability, with SHAP and LIME discussed as mechanisms to ensure that modeloutputs remain transparent and clinically meaningful 3,4.While the framework is not experimentally validated within this study, it is designed withpractical deployment considerations in mind. The discussion outlines a phased validationpathway, suggesting how such a system could be incrementally tested and refined in realworld settings. Overall, the work aims to contribute not just a technical perspective, but aclinically sensitive approach to early-stage cardiac risk awareness in women.