To the Editor of the Journal of Cardiovascular Electrophysiology, We read with great interest the article the recent publication titled ”Impact of Artificial Intelligence Arrhythmia Mapping on Time to First Ablation, Procedure Duration, and Fluoroscopy Use” by Fox et al [1]. The study investigates the effectiveness of AI-driven arrhythmia mapping in reducing time to first ablation, procedure duration, and fluoroscopy use in patients undergoing cardiac ablation. The authors conducted a retrospective analysis of 28 patients treated with traditional methods and compared the outcomes with those of 28 patients treated using the new AI mapping technology. They observed significant improvements in procedural efficiency and safety with the AI approach. While the results presented are promising, several aspects of the study deserve careful consideration to understand its full impact and limitations. First, the retrospective design of this study introduces potential biases, such as selection bias and information bias.Patients were not randomized, which could lead to differences between the control and experimental groups that are not related to the intervention. RCTs are needed to validate these findings and minimize these biases. Second, the study relies on historical controls, comparing patients treated with traditional methods in the past to those treated with AI mapping more recently. These temporal differences could introduce confounding variables, making it unclear whether the improvements are due to the AI mapping or other advancements in the field. Furthermore, the study does not provide detailed information on the specific AI algorithms used for arrhythmia mapping.Understanding the underlying technology is crucial for assessing the broader applicability of the results. Different AI models may have varying levels of effectiveness, and their performance can be influenced by the quality and type of data used for training and validation [2]. Finally, the study’s findings may not be generalizable to all clinical settings. The patient population, procedural techniques, and operator expertise at the study site may differ from those at other institutions. Further studies involving multiple centers and diverse patient populations are needed to confirm the broad applicability of the results. While the study by Fox et al. provides promising evidence of the benefits of AI in arrhythmia mapping, critical evaluation of the study design and methodology reveals several limitations that need to be addressed. Further prospective,randomized controlled trials are essential to validate these findings and ensure the robustness and generalizability of AI applications in clinical practice. This study represents an important step towards integrating AI into routine clinical practice, with the potential for significant improvements in patient outcomes and procedural efficiency. Sincerely, Federico Guerini References 1. Fox SR, Toomu A, Gu K, Kang J, Sung K, Han FT, Hoffmayer KS, Hsu JC, Raissi F, Feld GK, McCulloch AD, Ho G, Krummen DE. Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration,and fluoroscopy use. J Cardiovasc Electrophysiol. 2024 May;35(5):916-928. doi: 10.1111/jce.16237. Epub 2024 Mar 4. PMID: 38439119. 2. Loeffler SE, Trayanova N. Primer on Machine Learning in Electrophysiology. Arrhythm Electrophysiol Rev. 2023 Mar 28;12:e06. doi: 10.15420/aer.2022.43. PMID: 37427298; PMCID: PMC10323871.