Bioscience Methods 2024, Vol.15, No.3, 124-138 http://bioscipublisher.com/index.php/bm 132 incorporated demographic information and other clinical parameters to enhance its predictive accuracy. The AI model demonstrated high accuracy in distinguishing hypertensive from normotensive patients, with an area under the receiver operating characteristic curve (AUC) of 0.89 (Angelaki et al., 2022). 6.3 Outcomes and impact on patient care The implementation of the AI-based diagnostic system had a significant impact on patient care. The system's ability to accurately identify hypertensive patients allowed for earlier intervention and more personalized treatment plans. For instance, patients identified as high-risk by the AI algorithm were more likely to receive timely and appropriate antihypertensive therapy, reducing the risk of complications such as heart failure and stroke (Yao et al., 2021; Visco et al., 2023). Moreover, the AI system facilitated continuous monitoring of blood pressure (BP) using wearable technologies, enabling real-time adjustments to treatment plans based on changes in BP readings (Visco et al., 2023). This approach not only improved patient outcomes but also enhanced patient engagement and adherence to treatment regimens. In a clinical trial, the use of an AI-powered ECG tool increased the diagnosis of low ejection fraction (EF) in patients, demonstrating the potential of AI to improve the detection of other cardiac conditions associated with hypertension (Yao et al., 2021). The trial showed that patients in the intervention group, who had access to AI results, had a higher rate of new diagnoses of low EF compared to the control group (2.1% vs. 1.6%) (Yao et al., 2021). 6.4 Lessons learned and future directions The case study highlighted several key lessons in the implementation of AI-based diagnostic systems for HHD. The integration of AI into clinical workflows requires careful planning and collaboration between clinicians, data scientists, and IT professionals. Ensuring that the AI system is user-friendly and seamlessly integrated into existing EHR systems is crucial for its successful adoption. The accuracy and reliability of AI algorithms depend on the quality and diversity of the training data. In this case study, the use of a large and diverse dataset contributed to the high performance of the AI model. However, ongoing validation and refinement of the algorithm are necessary to maintain its accuracy and address potential biases (Angelaki et al., 2022; Visco et al., 2023). The use of AI in clinical practice raises important ethical and legal considerations, particularly regarding patient data privacy and the "black-box" nature of some AI algorithms. Transparent and explainable AI models, along with robust data governance frameworks, are essential to address these concerns (Visco et al., 2023). Looking ahead, future research should focus on expanding the application of AI-based diagnostics to other aspects of hypertensive heart disease, such as predicting the progression of the disease and identifying optimal treatment strategies. Additionally, large-scale clinical trials are needed to further validate the effectiveness of AI systems in improving patient outcomes and to explore their cost-effectiveness in routine clinical practice (Yao et al., 2021; Angelaki et al., 2022; Visco et al., 2023). In conclusion, the implementation of AI-based diagnostic systems for hypertensive heart disease holds great promise for enhancing early detection and personalized treatment. By leveraging advanced machine learning techniques and integrating them into clinical workflows, healthcare providers can improve patient outcomes and reduce the burden of hypertension-related complications. 7 Comparative Analysis: AI vs. Traditional Diagnostic Methods 7.1 Accuracy and sensitivity AI-based diagnostic systems have demonstrated superior accuracy and sensitivity in detecting various cardiovascular conditions, including hypertensive heart disease, compared to traditional diagnostic methods. For instance, AI algorithms applied to electrocardiography (ECG) data have shown high diagnostic accuracy for
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