Abstract Introduction Numerous artificial intelligence (AI) algorithms have been developed to assist in clinical decision-making, with a focus on areas like electrocardiogram (ECG) analysis due to the signal complexity and the varying levels of expertise among healthcare professionals. However, ensuring acceptance of these algorithms by end-users is paramount for successful implementation. Purpose This study aimed to identify barriers and facilitators related to implementation of an AI algorithm for ECG analysis and triage in patients with chest pain. Methods A three-round modified electronic Delphi study was conducted among Dutch potential end-users of an ECG-AI algorithm, including physicians, nurses and ambulance professionals. During the first round, participants brainstormed on barriers and facilitators, which were subsequently mapped to the Consolidated Framework for Implementation Research (CFIR). In the following two rounds, participants reviewed the identified barriers and facilitators for relevance and ranked them according to their importance. Results Twenty-five out of the initial forty respondents completed all three rounds, resulting in a dropout rate of 38%. Four barriers and twelve facilitators were identified. The most important facilitator was "Faster recognition of subtle ECG abnormalities", while "Poor model performance" emerged as the most important barrier. The most frequently mentioned corresponding CFIR domains were the innovation and inner setting domains. Conclusion The identification of barriers and facilitators provides valuable insights into the acceptance and utilisation of ECG-AI algorithms by end-users. By considering the CFIR domains, including innovation and inner setting, this study offers a comprehensive understanding of the contextual factors that influence the successful implementation of AI algorithms in healthcare settings. Addressing these barriers and facilitators will be crucial to facilitating future implementation processes.Barriers and facilitators