O. Inbar, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
O. Inbar, R. Dlin and R. Casaburi

Eur J Appl Physiol 2025 Mar 21

Cardiopulmonary exercise testing (CPET) has emerged as a powerful diagnostic tool, providing comprehensive physiological insights into the integrated function of cardiovascular, respiratory, and metabolic systems. Exploiting physiological interactions, CPET allows in-depth diagnostic insights. CPET performance entrains several complexities. Interpreting CPET data can be challenging, requiring significant physiological expertise. The advent of artificial intelligence (AI) has introduced a transformative approach to CPET interpretation, enhancing accuracy, efficiency, and clinical decision-making. This review article explores the current state of AI applications in CPET, highlighting AI’s potential to replace the traditional stress electrocardiogram (ECG) test as the preferred diagnostic tool in preventive medicine and medical screening. The article discusses the underlying principles of AI, its integration into CPET interpretation, and the associated benefits, including improved diagnostic accuracy, reduced interobserver variability, and expedited decision-making. Additionally, it addresses the challenges and considerations surrounding the implementation of AI in CPET such as data quality, model interpretability, and ethical concerns. The review concludes by emphasizing the significant promise of AI-assisted CPET interpretation in revolutionizing preventive medicine and medical screening settings and enhancing patient care.

Declarations. Conflict of interest: Dr. Omri Inbar is a retired senior lecturer from the Sackler School of Medicine at Tel-Aviv University and is currently the scientific consultant to Medibyt Ltd., external to submitted work. Or Inbar serves as the CEO of Medibyt LTD (Advanced CPET Analysis Platform), which is external to the submitted work. The remaining authors declare no competing interests.