Advancing personalized medicine in digital health: The role of artificial intelligence in enhancing clinical interpretation of 24-h ambulatory blood pressure monitoring

Abstract

<b>Background:</b> The use of artificial intelligence (AI) for interpreting ambulatory blood pressure monitoring (ABPM) data is gaining traction in clinical practice. Evaluating the accuracy of AI models, like ChatGPT 4.0, in clinical settings can inform their integration into healthcare processes. However, limited research has been conducted to validate the performance of such models against expert interpretations in real-world clinical scenarios. <b>Methods:</b> A total of 53 ABPM records from Mayo Clinic, Minnesota, were analyzed. ChatGPT 4.0's interpretations were compared with consensus results from two experienced nephrologists, based on the American College of Cardiology/American Heart Association (ACC/AHA) guidelines. The study assessed ChatGPT's accuracy and reliability over two rounds of testing, with a three-month interval between rounds. <b>Results:</b> ChatGPT achieved an accuracy of 87% for identifying hypertension, 89% for nocturnal hypertension, 81% for nocturnal dipping, and 94% for abnormal heart rate. ChatGPT correctly identified all conditions in 60% of ABPM records. The percentage agreement between the first and second round of ChatGPT's analysis was 81% in identifying hypertension, 85% in nocturnal hypertension, 89% in nocturnal dipping, and 94% in abnormal heart rate. There was no significant difference in accuracy between the first and second round (all p > 0.05). The Kappa statistic was 0.63 for identifying hypertension, 0.66 for nocturnal hypertension, 0.76 for nocturnal dipping, and 0.70 for abnormal heart rate. <b>Conclusions:</b> ChatGPT 4.0 demonstrates potential as a reliable tool for interpreting 24-h ABPM data, achieving substantial agreement with expert nephrologists. These findings underscore the potential for AI integration into hypertension management workflows, while highlighting the need for further validation in larger, diverse cohorts.

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