A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology
<b>Background</b>: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret complex datasets, improve diagnosis, optimize clinical workflows, and personalize therapy. ChatGPT-4o, a leading AI-based language model, exemplifies the transformative potential of AI in clinical research, medical education, and patient care. <b>Aim and Methods</b>: In this paper, we present an exploratory dialogue with ChatGPT to assess the role of AI in shaping the future of cardiology, with a particular focus on arrhythmia management and cardiac electrophysiology. Topics discussed include AI applications in ECG interpretation, arrhythmia detection, procedural guidance during ablation, and risk stratification for sudden cardiac death. We also examine the risks associated with AI use, including overreliance, interpretability challenges, data bias, and generalizability. <b>Conclusions</b>: The integration of AI into cardiovascular care offers the potential to enhance diagnostic accuracy, tailor interventions, and support decision-making. However, the adoption of AI must be carefully balanced with clinical expertise and ethical considerations. By fostering collaboration between clinicians and AI developers, it is possible to guide the development of reliable, transparent, and effective tools that will shape the future of personalized cardiology and electrophysiology.
Authors
- Nicola Pierucci (ORCID: https://orcid.org/0000-0002-5985-4081)
- Andrea Matteucci (ORCID: https://orcid.org/0000-0002-8831-6178)
- Elio Zito
- Angelica Cersosimo (ORCID: https://orcid.org/0000-0001-8463-8412)
- Vincenzo Mirco La Fazia (ORCID: https://orcid.org/0000-0001-7548-4685)
Institutions
- University of Rome Tor Vergata (IT)
- Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia (IT)
- St David's Medical Center (US)
- Texas Cardiac Arrhythmia (US)
- University of Brescia (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Journal of Personalized Medicine
- Published
- 2025-05-20
- DOI
- https://doi.org/10.3390/jpm15050205
- Citations
- 34
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 22.25