Bioscience Methods 2024, Vol.15, No.3, 124-138 http://bioscipublisher.com/index.php/bm 135 estimated from photoplethysmograph (PPG) signals obtained from smartphones or smartwatches (Visco et al., 2023). Additionally, AI models have been developed for the detection of various cardiovascular-related diseases, including arrhythmias and hypertension, with high sensitivity and specificity (Lee et al., 2022). Moreover, the integration of AI with omics-based technologies is paving the way for personalized medicine. By analyzing genetic, proteomic, and metabolomic data, AI can help identify new hypertension genes, enabling early diagnosis and prevention of complications (Visco et al., 2023). The development of novel biosensors and the application of AI in analyzing these biosignals further enhance the accuracy and actionability of cardiovascular diagnoses (Krittanawong et al., 2020). However, the implementation of these technologies in clinical practice requires addressing challenges related to data privacy, algorithm transparency, and the "black-box" nature of many ML models (Krittanawong et al., 2020; Visco et al., 2023). 9.2 Integration with wearable devices and remote monitoring Wearable devices and remote monitoring technologies are revolutionizing the management of hypertensive heart disease. These devices enable continuous, real-time monitoring of cardiovascular parameters, providing valuable data for early detection and intervention. AI-enhanced wearable sensors can monitor physiological signals such as electrocardiography (ECG), heart rate variability (HRV), and PPG, which are crucial for diagnosing and managing hypertension (Pires et al., 2021; Sharma et al., 2021). The integration of AI with these wearable devices allows for the development of predictive models that can forecast extreme events and generate timely alerts, thereby improving patient outcomes (Pires et al., 2021). Remote monitoring technologies, coupled with AI, have the potential to transform ambulatory care workflows. For instance, AI algorithms can analyze time-series data collected from wearable devices to predict heart failure exacerbations and other cardiovascular events (Gautam et al., 2022). This approach not only reduces hospitalizations but also enhances the quality of life for patients by enabling proactive management of their condition. However, the widespread adoption of these technologies faces challenges related to data integration, interoperability, and regulatory compliance (Krittanawong et al., 2020; Gautam et al., 2022). 9.3 Personalized medicine and ai-driven insights Personalized medicine, driven by AI, is poised to revolutionize the treatment of hypertensive heart disease. AI algorithms can analyze individual patient data, including genetic, environmental, and lifestyle factors, to develop tailored treatment plans. This approach aligns with the principles of 5P medicine (Predictive, Preventive, Participatory, Personalized, and Precision), which aims to provide personalized care based on individual patient profiles (Pires et al., 2021). By leveraging AI, healthcare providers can identify patient trajectories, predict disease progression, and adjust therapies accordingly (Visco et al., 2023). AI-driven insights also facilitate the development of personalized medication regimens. For example, AI can analyze patient responses to different antihypertensive drugs and recommend the most effective treatment with minimal side effects (Visco et al., 2023). Additionally, AI can help in identifying patients at high risk of developing hypertension-related complications, enabling early intervention and prevention strategies (Pires et al., 2021; Visco et al., 2023). The integration of AI with wearable devices and remote monitoring technologies further enhances personalized medicine. These technologies provide continuous data streams that AI algorithms can analyze to offer real-time insights and recommendations. For instance, AI can monitor a patient's BP trends and suggest lifestyle modifications or medication adjustments to maintain optimal BP levels (Sharma et al., 2021; Pires et al., 2021). However, the successful implementation of personalized medicine requires addressing ethical and legal issues related to data privacy, algorithmic bias, and patient consent (Krittanawong et al., 2020; Huang et al., 2022). In conclusion, the future of AI-based diagnostic systems for hypertensive heart disease is promising, with emerging technologies, wearable devices, and personalized medicine driving significant advancements. Continued research and collaboration between device designers, clinical researchers, and regulatory bodies are essential to
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