Physical examination and artificial intelligence tools in the work of specialists managing arrhythmias: frequency of use and level of trust

Abstract

Aim . To assess the frequency of use and level of trust in physical examination (PE) methods, as well as the degree of artificial intelligence (AI) integration into the practice of specialists managing arrhythmias. Material and methods. An anonymous online survey of 143 respondents (cardiology and surgical physicians and residents) was conducted. The questionnaire included questions on professional and demographic data, frequency of use and trust in nine PE methods, and the use of AI tools. Statistical analysis was performed in RStudio using Fisher’s exact test, Pearson’s χ2 test, Spearman/Pearson correlation analysis, and PAM (Partitioning Around Medoids) (Gover distance) clustering. R e sults . The analysis included 127 fully completed questionnaires (88% physicians, 12% residents). The most frequently used PE methods were cardiac/lung auscultation and blood pressure measurement, while the least frequently used were cardiac percussion and chest palpation. Frequency of use correlated with the type of work as follows: cardiologists used a wider range of techniques, while invasive specialists (radiologists, cardiovascular surgeons) used only a limited number of FEs. The main reasons for not using FEs were the availability of paraclinical methods (41,7%) and time constraints (44,9%). Only 40% of respondents use AI in their work, primarily for information retrieval and writing. Cluster analysis revealed three following specialist phenotypes: "conservative" (broad physical examination, no AI), "technology-oriented" (selective physical examination, active AI), and "surgical" (narrowly selective physical examination). Conclusion . A following significant gap in diagnostic approaches was identified: a decline in physical skills among young and highly specialized physicians is combined with a predominantly "technical" use of AI. The obtained data substantiate the need to integrate a hypothesis-driven approach to physical examination and AI training into medical education programs to maintain clinical competence.

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