Clinicians' ethical considerations on use of AI-enabled technologies for primary prevention of cardiovascular disease in female patients

Abstract

Artificial intelligence (AI) is increasingly used in healthcare to support the prevention and management of cardiovascular disease (CVD); however, its ethical implications in clinical practice, particularly for female patients, remain insufficiently explored. This study aimed to explore clinicians' perspectives on the ethical use of AI for preventing and managing cardiovascular disease (CVD) in female patients. A qualitative descriptive design was employed using semi-structured interviews with clinicians practicing in Montreal, Canada. Interviews were conducted online, audio-recorded with participants’ consent, and transcribed for analysis. Data were analyzed using deductive thematic analysis informed by ethical domains in the established AI frameworks. Ethical approval was obtained from McGill University’s Research Ethics Board. The study adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. A final sample of twelve clinicians was interviewed, with each interview lasting approximately 60 minutes. Four key themes emerged: fairness, privacy and security, explainability, and data integrity. Clinicians expressed concerns that AI-enabled technologies may introduce or reinforce biases affecting certain populations, including older adults, individuals with limited digital literacy, and those lacking reliable internet access or access to digital technologies. Participants also raised concerns regarding data integrity, privacy and security, and emphasized the importance of transparent and understandable AI outputs to support clinical decision-making. Ethical considerations are fundamental to the responsible integration of AI in cardiovascular care. Addressing concerns related to fairness, privacy and security, explainability, and data integrity may strengthen clinician trust and support the implementation of AI-enabled technologies in clinical practice. Future research should explore practical approaches to address these concerns and assess how ethically informed AI systems can be implemented effectively in clinical practice.

Ähnliche Arbeiten