{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T17:23:24Z","timestamp":1787678604240,"version":"build-2784847793"},"reference-count":92,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The growing integration of personalized risk prediction (PRP) and AI substantially reshapes diagnostic and therapeutic decision-making in health care. At the same time, its responsible adoption depends not only on technical performance, but also on patients\u2019 perspectives and acceptance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study systematically examined patients\u2019 perspectives across several European countries and explored how patients\u2019 technology-related attitudes relate to their evaluations of personalized and AI-supported approaches in cardiac care. As part of the PROFID (Prevention of Sudden Cardiac Death After Myocardial Infarction by Defibrillator Implantation) project, its focus is on the ethical use of PRP and AI in the clinical context of decision-making regarding sudden cardiac death (SCD) prevention and implantable cardioverter-defibrillator (ICD) implantation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The study used a cross-sectional survey design with a standardized questionnaire including multimedia content. The target population comprised adults aged 18 years or older living in 6 European countries who met at least one of the following (self-reported) clinical criteria: heart failure, myocardial infarction (MI), cardiac arrest, or current ICD implantation. An exploratory factor analysis (EFA) was used to identify and evaluate internally consistent factors, and subsequent regression analyses examined associations between these factors and technological openness, sociodemographic characteristics, and patients\u2019 views on PRP and AI in cardiac care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The sample consisted 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). Overall, 51.9% (244\/470) of respondents were male and 48.1% (226\/470) were female. The mean age of the sample was 61.12 (SD 12.62) years. The EFA showed six clearly interpretable factors: (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 regression analysis showed the relations of technological readiness, self-reported AI literacy, support for delegation of decisions to algorithms, transparency expectations in algorithmic decision-making, preferences for SDM, educational attainment, gender, and age to find associations with patients\u2019 perceived benefits and support of PRP or AI in MDM.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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. PRP and AI were generally perceived as useful tools to support decision-making regarding ICD indication, provided transparency is ensured and patients remain actively involved in the decision-making process. Mandatory use and full delegation to decision-making directly by AI were broadly rejected. The attributed acceptance of delegation to PRP models was significantly higher than AI. In summary, implementation should support empathetic communication, patient involvement, and individual and institutional responsibility.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/96968","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:10:07Z","timestamp":1781194207000},"page":"e96968-e96968","source":"Crossref","is-referenced-by-count":0,"title":["Patients\u2019 Perspectives on Applications of AI and Personalized Medicine in Life-Threatening Heart Disease: European Cross-Sectional Patient Questionnaire Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6837-3319","authenticated-orcid":false,"given":"Georg Ludwig","family":"Lindinger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2817-938X","authenticated-orcid":false,"given":"Nicolas J","family":"Schiermeier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5587-2549","authenticated-orcid":false,"given":"Dick L","family":"Willems","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8907-3221","authenticated-orcid":false,"given":"Menno Tom","family":"Maris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2918-5772","authenticated-orcid":false,"given":"Mona","family":"Khattab","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7905-5818","authenticated-orcid":false,"given":"Hanno L","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8032-3883","authenticated-orcid":false,"given":"Dennis","family":"Henzler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0655-0743","authenticated-orcid":false,"given":"Marieke A R","family":"Bak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5950-2677","authenticated-orcid":false,"given":"Eckhard","family":"Nagel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7267-2303","authenticated-orcid":false,"given":"Michael","family":"Lauerer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"issue":"4","key":"R1","doi-asserted-by":"publisher","DOI":"10.2196\/14689","article-title":"Perception of the progressing digitization and transformation of the German health care system among experts and the public: mixed methods study","volume":"5","author":"Hansen","journal-title":"JMIR Public Health Surveill"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.glmedi.2024.100108","article-title":"Artificial intelligence in healthcare delivery: prospects and pitfalls","volume":"3","author":"Olawade","journal-title":"J Med Surg Public Health"},{"issue":"12","key":"R3","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering10121435","article-title":"How artificial intelligence is shaping medical imaging technology: a survey of innovations and applications","volume":"10","author":"Pinto-Coelho","journal-title":"Bioengineering (Basel)"},{"key":"R4","unstructured":"Steyerberg EW . 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