Patients' Perspectives on Applications of AI and Personalized Medicine in Life-Threatening Heart Disease: European Cross-Sectional Patient Questionnaire Study.

Six distinct interpretable factors emerged from exploratory factor analysis of patient attitudes toward AI and personalized medicine in cardiac care.

• The six factors were: (1) perceived benefits and support of PRP models in medical decision-making (MDM), (2) perceived benefits and support of AI in MDM, (3) transparency expectations in algorithmic decision-making, (4) support for delegating decisions to algorithms, (5) self-reported AI literacy, and (6) preference for shared decision-making (SDM).
• The EFA was applied to a cross-sectional survey sample of 470 participants from Germany (n=210), the Netherlands (n=86), the United Kingdom (n=145), and 3 other European countries (n=29; Austria, Belgium, and Spain).
• The sample was 51.9% male (244/470) and 48.1% female (226/470) with a mean age of 61.12 (SD 12.62) years.
• Participants met at least one self-reported clinical criterion: heart failure, myocardial infarction, cardiac arrest, or current ICD implantation.

Patient acceptance of delegation to personalized risk prediction (PRP) models was significantly higher than acceptance of delegation to AI.

• The attributed acceptance of delegation to PRP models was significantly higher than to AI.
• Mandatory use and full delegation of decision-making directly to AI were broadly rejected by participants.
• This distinction suggests patients differentiate between statistical/predictive models and AI as a concept in clinical decision-making contexts.
• The finding pertains specifically to the clinical context of sudden cardiac death (SCD) prevention and ICD implantation decisions.

Technological readiness, self-reported AI literacy, and support for delegation to algorithms were associated with patients' perceived benefits of PRP or AI in medical decision-making.

• Regression analyses examined associations between the identified factors and technological openness, sociodemographic characteristics, and patients' views on PRP and AI in cardiac care.
• Variables showing associations included: technological readiness, self-reported AI literacy, support for delegation of decisions to algorithms, transparency expectations in algorithmic decision-making, preferences for shared decision-making (SDM), educational attainment, gender, and age.
• Both PRP and AI perceived benefit outcomes were examined as dependent variables in the regression analyses.
• The study used a standardized questionnaire including multimedia content to present these concepts to patients.

Transparency expectations and shared decision-making preferences were identified as key factors shaping patient evaluations of AI and PRP in cardiac care.

• Transparency expectations in algorithmic decision-making emerged as a standalone factor (Factor 3) in the EFA.
• Preference for shared decision-making (SDM) was identified as a separate factor (Factor 6).
• Both transparency and SDM preferences showed associations with patients' perceived benefits and support of PRP or AI in MDM in subsequent regression analyses.
• PRP and AI were perceived as useful only provided transparency is ensured and patients remain actively involved in the decision-making process.

Sociodemographic characteristics including educational attainment, gender, and age were associated with patient perspectives on AI and PRP in cardiac decision-making.

• Educational attainment, gender, and age were all included as variables showing associations in the regression analyses.
• The sample mean age was 61.12 (SD 12.62) years, reflecting the cardiac patient population studied.
• The study was conducted across six European countries, capturing cross-national variation in patient perspectives.
• These sociodemographic associations highlight that patient acceptance of high-level technologies is not uniform across patient subgroups.

The study was conducted as part of the PROFID project, focusing on ethical use of AI and personalized risk prediction in the context of sudden cardiac death prevention and ICD implantation decisions.

• PROFID stands for 'Prevention of Sudden Cardiac Death After Myocardial Infarction by Defibrillator Implantation.'
• The study used a cross-sectional survey design with a standardized questionnaire including multimedia content.
• Target population comprised adults aged 18 years or older in 6 European countries meeting at least one of: heart failure, myocardial infarction, cardiac arrest, or current ICD implantation.
• Total sample was N=470 participants across Germany, the Netherlands, the United Kingdom, Austria, Belgium, and Spain.

The authors concluded that implementation of AI and PRP in cardiac care should support empathetic communication, patient involvement, and individual and institutional responsibility.

• These recommendations emerged from findings that mandatory use and full AI delegation were broadly rejected.
• The findings 'support existing assumptions while also highlighting additional aspects that should be considered if high-level technologies are used in decision-making processes related to ICD implantation.'
• The study's conclusions emphasize that responsible adoption depends 'not only on technical performance, but also on patients' perspectives and acceptance.'
• Empathetic communication was specifically named as an implementation requirement alongside patient involvement and accountability structures.