Role of Artificial Intelligence (AI) in Clinical Interpretation of 24-Hour Ambulatory Blood Pressure Monitoring

Background: The utility of AI in interpreting ambulatory blood pressure monitoring (ABPM) data is increasingly recognized. 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 clinical scenarios. Methods: This study assessed the performance of ChatGPT 4.0 in interpreting 24-hour ABPM records from 54 cases at Mayo Clinic, Minnesota, in March 2024. The AI's interpretations were compared with results confirmed by two nephrologists, who reached a consensus on the presence or absence of specific conditions based on the American College of Cardiology/American Heart Association (ACC/AHA) guidelines. Results: ChatGPT 4.0 demonstrated varied accuracy across different conditions: Hypertension (84.91%), Hypotension (94.34%), Nocturnal Hypertension (81.13%), Normal Nocturnal Dip (75.47%), Normal Heart Rate (90.57%), Tachycardia (73.58%), and Bradycardia (71.70%). These outcomes highlight the model's capability to reliably interpret complex clinical data with considerable accuracy. The model's performance in identifying tachycardia and bradycardia, however, was comparatively lower, suggesting room for improvement in these areas. Conclusion: The findings suggest that ChatGPT 4.0 has the potential to assist in the clinical interpretation of 24-hour ABPM. However, the model's current performance indicates that further improvements in accuracy are necessary before it can be effectively incorporated into clinical practice. Ongoing advancements and training on diverse datasets could enhance the model's diagnostic precision, making AI a promising tool for the routine analysis of ABPM data. While the results are encouraging, further research is needed to refine the model's performance and validate its utility across larger, more diverse patient populations.